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Mon, 10 Oct 2022 12:03:00 -0500 Todd Bishop en-US text/html https://www.geekwire.com/tag/adobe-analytics/
Killexams : A guide to Google Analytics 4 for marketing agencies

On July 1, 2023, Google will move everyone to its latest version, Google Analytics 4 (GA4), and retire Google Analytics 3 (also known as Universal Analytics or UA). While these changes will benefit the average user without any noticeable difference in how they search and browse online, the switch will require significant changes for marketers and businesses.

Here’s everything you need to know about Google Analytics 4, including what it will mean for how you measure marketing activity and conversions, how to get started using GA4 and how to prep your clients for the change.

What is Google Analytics 4?

Google Analytics is a staple tool for marketers to track online activity. If you’ve used Google Analytics in the past, GA4 will look familiar.

So what’s the big difference?

GA4 changes how data is collected and reorients the metrics from sessions to events. This combines users’ web and mobile app data to more seamlessly measure their journey across platforms. GA4’s data collection also takes into account the increasing concerns consumers have around privacy and, in particular, cookie tracking. 

GA4 is currently available (and the default if you set up a new property), but many marketers still rely on Universal Analytics. Additionally, since GA4 is still being updated, everyone is in the same boat, learning how to use the new metrics. Companies that integrate with Google Analytics must update their integrations before the July 2023 deadline, and this includes CallRail. We are currently revamping our Google Analytics integration, so you can continue to report on and analyze call data in Google Analytics and provide more insight into visitor interactions than ever before.

Does GA4 use cookies?

Yes and no.

If you’ve worked in marketing during the past few decades, you know the importance of cookies in helping you measure your goals and advertise your brand. So it might seem jarring to think GA4 is messing with cookies at all.

The short version is that Google Analytics 4 relies on first-party cookies while restricting third-party cookies. GA4 also adds signals to the mix, which is session data from sites and apps that Google associates with users who have signed into their Google accounts and turned on Ads Personalization.

Why is that? Let’s recap what a cookie is first.

Cookies are a way for your computer to remember where you’ve been and what you’ve done on a site and to communicate that back to the site. This makes for a more personalized experience and allows marketers to track engagement.

Third-party cookies are unique because they allow the sites to track users beyond the property. Whole industries grew out of advertising using third-party cookies, but the practice has come under scrutiny from regulators and privacy-conscious consumers. When the European Union’s General Data Protection Regulation (GDPR) took effect in 2018, it kicked off a shift in the way third-party cookies are treated.

By removing support for third-party cookies, GA4 actually beats Google’s browser, Chrome, to the punch. Chrome, the world’s most popular browser, will end third-party cookie support at the end of 2023.

Privacy isn’t the only reason that GA4 is moving away from third-party cookies. As more people use mobile devices to access the internet, more users are foregoing the web in lieu of apps. In fact, in 2021, 90% of mobile time was spent using apps, not the web. That’s a huge shift, and when paired with the death of third-party cookies, it became clear to Google that Universal Analytics wasn’t built for that reality.

GA4 vs. Universal Analytics

Should I use Universal Analytics or GA4?

For now, you have a choice between GA4 and UA. If you’re setting up a new Google Analytics property, it will default to GA4, but you can choose to only use UA through some advanced options during setup.

We recommend using both for now, for several reasons.

Despite being out of its beta, GA4 is still constantly being improved with added features. Moving over now may provide a false sense of what life with just GA4 will really be like.

UA metrics won’t align 1:1 with GA4 metrics. By having both, you can see how your key measurements will be affected by the change and alter your reporting accordingly. For example, if you rely on Bounce Rate to track whether a page is performing well, you’ll lose that in GA4. Instead, you’ll have an Engagement Rate, which cannot be considered the inverse of Bounce Rate because it has a time threshold associated with it.

By waiting to move away from UA, you’ll retain your key integrations with Google Analytics, such as CallRail’s Google Analytics integration.

By leveraging elements of both Universal Analytics and Goole Analytics 4 into your client reporting now, clients will get used to the new system and have time to adjust before transitioning completely to GA4 in 2023.

Ultimately, of course, you’ll be using GA4. But until then, use this time as an opportunity to learn about GA4 without sacrificing your current reports or third-party GA integrations.

What do I gain and lose by upgrading?

With a big change like Google Analytics 4, there are going to be some things that feel like improvements and some things that feel like downgrades. Time will tell what the changes will mean for your business and your clients, but we know the effects of some already.

Here’s what you’ll gain with Google Analytics 4:

  • Event-based tracking: This one could easily go in the “lose” column depending on how you feel about UA’s measurement model of sessions and pageviews. But event-based tracking brings together web and app engagement for a more holistic view of the user, with the potential for richer journey insights.
  • Better reporting and analysis: GA4 borrows from Google Data Studio to provide simple-to-use templates for custom reporting.
  • Automated insights: Artificial intelligence and machine learning are going to highlight new insights for you.

Here’s what you’ll lose when you switch:

  • Historical data: Your historical data in UA (as well as your tags) won’t migrate over to GA4. Since GA4 requires a new property, you’ll essentially be starting from scratch.
  • Your conversions: Since the underlying measurements are changed, your conversions will be different now too.
  • Views: As of now, GA4 doesn’t provide views, which UA users could deploy to configure tests or filter internal traffic from the data.
  • Limits on filters and customer dimensions: IP and hostname filtering have been limited or deprecated and custom dimensions are limited to 50.
  • Third-party integrations: Third-party integrations into GA for everything from your CRMs, to your e-commerce, to your CMS’ that were built on UA’s measurements will no longer work until they’re updated to GA4.

For a full breakdown of everything you need to know before switching to GA4, including what it will mean for the way you measure marketing activity and conversions, how to get started using GA4 and how to prep your clients for the change, download our full guide now. 

See what CallRail’s call tracking can do to enrich your understanding of the customer journey when combined with your web visitor data in Google Analytics. Get started with a free trial today.

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Wed, 12 Oct 2022 23:49:00 -0500 CallRail en text/html https://searchengineland.com/a-guide-to-google-analytics-4-for-marketing-agencies-388505
Killexams : Adobe’s AI analytics play, VideoAmp by the second, and more on Harry Styles’ mega fame: Datacenter Weekly

Welcome to Ad Age Datacenter Weekly, our data-obsessed newsletter for marketing and media professionals.

Measuring advertising down to the second

“VideoAmp is rolling out second-by-second ad viewership as a deal currency, drawing on experience with Paramount’s BET as an early adopter measuring viewership not only of commercials but also longer-form branded content during its BET Awards last summer,” Ad Age’s Jack Neff reports.

The details: VideoAmp’s measurement, Neff notes, “combines input from smart TVs and cable or satellite set-top boxes covering more than 39 million homes.”

Essential context: “Second-by-second reporting for TV—in all its many forms—is still relatively rare for use as currency in deals, as ratings from Nielsen are still done based on average commercial minute viewing across programs or commercial pods,” Neff notes. “But Nielsen last year announced plans to roll out individual commercial measurements (still planned as part of the rollout of Nielsen One later this year, a spokesman said). And other measurement firms—including Comscore and iSpot.tv—previously have touted their ability to measure ads on a second-by-second basis.”

Keep memorizing here.

Adobe brings AI to marketing mix modeling

“Adobe is launching a marketing mix modeling service that uses artificial intelligence to assess return on investment in weeks rather than the months it typically takes for such analytics,” Ad Age’s Jack Neff reports.

The details: “Marketers can use the tool, which will be generally available as part of the Adobe Experience Cloud, to adjust media and marketing plans on the fly, or at least within a month or quarter, rather than taking a retrospective look at what happened in the past to adjust future spending.”

Essential context: “Adobe’s move comes as marketing mix modeling (MMM) enjoys a renaissance after years of losing ground to multi-touch attribution (MTA),” Neff adds. “Making the AI tool available across an already huge base of Adobe could fuel the growth of MMM further.”

Keep memorizing here.

More Harry Styles data

After we ran an item last week titled “Harry Styles’ mega fame by the numbers,” a bunch of Datacenter Weekly readers told us that they knew Styles was popular, but didn’t realize he was that popular. Well, folks, brace yourself for a few more bullet points:

• With Styles’ single “As It Was” having racked up 15 consecutive weeks at No. 1 on the Billboard Hot 100 singles chart, he is now the longest-reigning British artist to hold that distinction, having dethroned Elton John, whose 1997 “Candle in the Wind” spent 14 weeks at No. 1.

• The 2021 portion of Styles’ continuing “Love on Tour” stadium concert tour grossed nearly $95 million from 720,000 tickets sold across 42 shows—but the artist’s momentum has only accelerated since then. Last year, for instance, Styles was able to sell out New York City’s Madison Square Garden for five nights, but in returning to the iconic venue this past August, he sold out 15 nights (as we noted last week). But there’s more—much more: The 2022 leg of “Love on Tour” includes not only an additional 42 North American shows (which started in August and continue through November), but the 23 shows he played in Europe in July and August, as well as 14 shows coming up in Latin America in November and December.

• It’s entirely reasonable to expect that “Love on Tour” will bring in at least an additional $155 million or so by the end of 2022, which would bring the combined 2021-2022 gross above the quarter-billion-dollar mark. (Side note: “Love on Tour” continues in 2023 with dates in Australia—and Europe once again.)

A post-cookie data glossary

ICYMI: Privacy sandbox. Seller-defined audiences (SDAs). Match rate. Conversions API (CAPI) ...

If you want quick, no-nonsense definitions of those terms (and others), head over to “Post-cookie data glossary—key words and terms marketers need to know,” from Ad Age’s Garett Sloane.

Previously: “12 ad tech and data executives leading the cookieless evolution,” also from Ad Age’s Sloane.

Ad Age Leading National Advertisers 2022

In his introduction to the Ad Age Leading National Advertisers 2022 report, Ad Age Datacenter’s Bradley Johnson reports that advertisers scored “the second-biggest spending gain on record” in 2021, marking “an extraordinary turnaround from the pandemic plunge in 2020. Spending has continued to grow in 2022, though budgets could come under pressure as marketers grapple with inflation, rising interest rates and slumping consumer confidence amid escalating expectations of a recession.”

There’s a lot to LNA 2022—so the Datacenter team has come up with multiple entry points for you to make your own deep dive. To wit:

“LNA 2022—10 most-advertised brands in the U.S., ranked”
“LNA 2022—Will ad spending rise in the (coming) recession? It’s happened before”
“LNA 2022—25 biggest U.S. advertisers, ranked”
“LNA 2022—U.S. market leaders and category rankings”
“LNA 2022—Big spending gains and cuts”
“LNA 2022—What comes next after 2021's ad spending surge”
“LNA 2022—Ad spending by medium, category and advertiser”

The newsletter is brought to you by Ad Age Datacenter, the industry’s most authoritative source of competitive intel and home to the Ad Age Leading National Advertisers, the Ad Age Agency Report: World’s Biggest Agency Companies and other exclusive data-driven reports. Access or subscribe to Ad Age Datacenter at AdAge.com/Datacenter.

Ad Age Datacenter is Kevin Brown, Bradley Johnson and Joy R. Lee.

This week’s newsletter was compiled and written by Simon Dumenco.

Fri, 07 Oct 2022 07:01:00 -0500 en text/html https://adage.com/article/datacenter/adobes-ai-analytics-play-videoamp-second-harry-styles-mega-fame/2440406
Killexams : Your guide to Google Analytics 4 attribution

Nowadays, conversion is usually preceded not just by one but several interactions with a website or an app.

Attribution determines the role of each touchpoint in driving conversions and assigns credit for sales to interactions in conversion paths.

As Google’s deprecation of Universal Analytics (UA) nears, it’s crucial to understand attribution in Google Analytics 4 (GA4) – including what is new, what is missing, and what the differences mean for search marketers.

(If you are new to attribution, read the Google Analytics help article on attribution first.)

How Google Analytics 4 attribution works

Universal Analytics reports attributed the entire credit for the conversion to the last click. A direct visit is not considered a click, but for the avoidance of doubt, this attribution model was also called the last non-direct click model. Other attribution models were only available in the Model Comparison Tool in the Multi-Channel Funnels (MCF) reports section.

GA4 offers a wider availability of different attribution models, but it depends on the scope of the report – whether it is the user acquisition source, session source or event source. 

In Universal Analytics, the source dimensions had session scope solely. The MCF reports made it possible to analyze the sources of all sessions on the conversion path. The three scopes of source dimension in GA4 (user, session, event) are the most important and fundamental changes in the attribution area.   

This guide will use the term “source” in a broader meaning as any dimension that indicates the origin of a visit, e.g., channel grouping, source, medium, ad content, campaign, ad group, keyword, search term, etc.

Session source

Session-scope attribution – unsurprisingly – determines the source of the session. It is used, among others, in the Traffic acquisition reports in the Reports section. It works similarly to Universal Analytics in always using the last non-direct click model.

The session source is the source that started the session (e.g., social media referral or organic search result). However, if a direct visit started a session, the session source will be attributed to the source of the previous session (if there was any). 

Quick reminder: A direct visit means that Analytics does not know where the user came from because the click does not pass the referrer, gclid, or UTM parameter.

Therefore, exactly as it was in Universal Analytics, the session source will be direct only if Analytics cannot see any other source of visit for the given user within the lookback window. The default lookback window in GA4 is 90 days, while in Universal Analytics, it was six months by default. We will return to the lookback window matter later in this article.

By the way, what is a session?

A Google Analytics session is not the same as a browser session.

In GA4, a session begins when a user visits the website or app and ends after the user’s inactivity for a specified time (30 minutes by default – see this Analytics help article).

Closing the browser window does not end the session. If the browser window is closed, another visit to the website within the time limit would still belong to the same session – unless the browser deletes cookies and browser data after closing the browser window, for example in incognito mode.

In Universal Analytics, when a user re-visits the website from a new source during an existing session, the existing session is terminated, and a new session starts with that new source. 

In GA4, it is no longer the case. If a visit from a new source occurs during a session, a new session will not start, and the source of the current session will remain unchanged.

It does not mean that the visit from the new source is ignored. GA4 records the source of this visit, and the event-scope attribution reports (more on that later in this article) will take into account all sources of all sessions. (See this Analytics help article.)

A new visit during an existing session may happen, for example, if a user returns from a payment gateway or a webmail site after password recovery or registration confirmation. In GA4, these visits will not artificially inflate the number of sessions, as in Universal Analytics. 

Nevertheless, sources of these visits are so-called unwanted referrals and should be excluded. Visits from excluded referrals are reported as direct visits.

In GA4, these visits are de facto ignored because the session source and the session count remain unchanged. The non-direct attribution modeling in GA4 will assign no credit to this (direct) source (as described later in this article).

In Universal Analytics, the session (regardless of duration) ends at midnight, which is no longer the case in GA4.

First user source 

First user source (source of the first visit) is new to GA4. It shows where the user came from to the website or app for the first time.

It is a part of Google’s new approach to measurement in online marketing, which no longer focuses only on the classic ROAS (revenues vs. costs), but also analyzes the CAC vs. LTV (customer acquisition cost vs. lifetime value).

This approach reflects the app logic: we have to acquire the app user first, and after the app is installed, further marketing efforts engage and monetize the user. However, for the web traffic, it also makes more sense. 

The new customer acquisition goal in Google Ads, available in Performance Max campaigns, also represents a similar approach. In this case, the focus is on the first-time buyer, not the first visit. 

In GA4, the first user visit is recorded by the first_visit event for the website or the first_open event for the app. The naming is self-explanatory.

Therefore, the source of the first visit is a user attribute and indicates where this user’s first visit to the website or application came from.

The first visit source is attributed using the last non-direct click model. Of course, this attribution applies only to interactions before the first website visit or the first open of the app (interactions following the first visit or first open are not taken into account).

Once assigned, the source of the first visit remains unchanged – of course, as long as Google Analytics can technically link the user’s activity on the website and in the app with the same user.

The first user source will be reset if the tracking of the user is lost, for example, if the user does not visit the website for a period longer than the Analytics cookie expiration date.

We will return to the Analytics cookie expiration period and other data collection limitations in GA4 later in this article.

Event scope attribution

In GA4, events replaced sessions as the fundament of data collection and reporting. GA4 makes it possible to report attribution using a selected attribution model for any event (not only for conversions).

The model is set in the Attribution Settings of the GA4 property. There are several pre-defined models to choose from (see the screen below).

Google Analytics 4 Attribution Settings - Pre-defined models.

The default data-driven model can be changed at any time. This change is retroactive (i.e., it will also change the historical data).

A common belief is that Google Analytics 4 no longer uses the last-click attribution model. But is that the case?

In practice, it applies only to customized reports that use event-scope dimensions and metrics, for example, Medium – Conversions.

The default traffic and user acquisition reports use session source and first user source, respectively, and these dimensions use the last click model. It is indicated in the dimension name (e.g., Session – Campaign or First User – Medium).

Remember: source, session source and first user source are three different dimensions where different attribution models apply.

Scope Attribution Model Where available
Session Last click E.g., traffic acquisition reports
User (first user source) Last click E.g., user acquisition report
Event Model set in the GA4 property settings (data-driven by default) E.g., in the Explore section

Attribution settings

The attribution model set in the property settings applies to all reports in the property.

There are several attribution models, known from Universal Analytics (described in the earlier mentioned Analytics help article), to choose from. However:

  • All the models do not assign value to direct visits unless there is no other choice because there is no other interaction on the path. In other words, they all use the non-direct principle, which was not the case in the Universal Analytics pre-defined attribution models, except for the last non-direct click model. 
  • The Ads-preferred models assign the entire conversion value to Google Ads interactions if they occur in the funnel. At the moment, there is only one Ads-preferred model available – the last click model, which is the equivalent of the “last Google Ads click” known from Universal Analytics. In the absence of Google Ads interactions on the funnel, this model works like a regular last-click model.
  • In addition to clicks, models take into account “engaged views” of YouTube ads, that is, watching the ad for 30 seconds (or until the end if the ad is shorter) and other clicks associated with that ad (see this Google Analytics help article for more details).

Again, a change of the attribution model settings works retroactively (i.e., it applies to the historical data before the change). Saved explorations will be recalculated when viewing them.

Lookback window

Google Analytics property settings determine the length of the lookback window. The lookback window determines how far back in time a touchpoint is eligible for attribution credit. The default lookback window is 90 days, but you can change it to 60 or 30 days.

According to Analytics documentation, the lookback window settings apply to all attribution models and all conversion types in Google Analytics 4 (i.e., it also applies to session-level attribution and attribution model comparisons).

The lookback window of the first user source has a separate setting (30 days by default, and it can be changed to 7 days). Are you wondering why it is defined differently? 

Well, first of all, it is worth considering why there is any lookback window for the first visit at all.

Moreover, why are we talking about the first user attribution model, which is always the last (non-direct) click?

After all, GA4 knows the source of the first visit when this visit happens. As it is the first visit, there are no previous visits, and thus no other sources to consider.

So, what is the point of looking deeper in time than the first interaction with a website or app?

The answer is Google Signals. If this option is enabled for the GA4 property in the Data Collection settings, GA4 will enrich the data collected by the tracking code with, among others, information known by Google about logged-in users.

For example, Google may know that the user had an engaged interaction with our YouTube ad on a different device before the first visit.

Similarly, the user may use the app for the first time (first_open) during a direct session, but the install itself may result from a mobile app install campaign in Google Ads, clicked a few days earlier. 

Therefore, if the source of the first visit session is unknown (it is a direct visit), Google Analytics may try to assign the source of the first visit to the earlier known interaction if it occurred during the lookback window period.

In other words, thanks to Google Signals, GA4 may record ad interactions before the first user visit.

Lookback window changes do not work retroactively. It means that they only apply from the moment of the change.

The engaged views of YouTube ads, however, always have three days lookback window, regardless of the property settings.

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It is a nuance but worth noting. Universal Analytics's default lookback window for the acquisition reports was six months, and any change to this period was also non-retroactive. 

Such a change, however, did not apply to conversions but to interactions that had taken place after the change. It reflected the logic of the _utmz cookie, which was responsible for storing the source information.

Its expiration time was set when the cookie was created or updated (i.e., upon a visit from a given source). Universal Analytics no longer uses the _utmz cookie (it was used in earlier versions), but the logic was maintained for data consistency.

For example, changing the lookback window in Universal Analytics from 30 to 90 days did not immediately include interactions from 90 days ago in the acquisition reports for the visits since the date of the change because the virtual "source cookie" for interactions older than 30 days has already "expired."

There was a transition period (in this example, 90 days), after which all conversions were fully reported under the new lookback window. 

Google Analytics 4 uses a different data model, with no continuity with the UA data. They could therefore break with this past and stop using the cookie logic.

For example, they could apply changes to all conversions that have taken place since the change, as it is now in Google Ads. Interpreting such would be much easier. They could, but they did not. 

In GA4, the change applies to interactions still in the lookback window. 

For example, if the lookback window is increased from 30 to 90 days, the conversions will not immediately be reported in the new, 90 days lookback window. It will be reflected in the reports after 60 days from the date of change (the interactions from the initial 30-day lookback window will be remembered).

Reducing the lookback window (e.g., from 90 to 30 days) will apply the change immediately (i.e., all conversions will be reported in the shorter, 30 days window). 

Yes, it sounds exotic. Fortunately, in practice, the analysts do not change the lookback window often. 

The Google Analytics 4 cookie has a standard expiration time of 24 months, but it can be changed to a period between one hour and 25 months (or the cookie may be set as a session cookie and expire after the browser session end).

Subsequent visits may renew this time limit. This will be the period in which Analytics will be able to recognize a returning user and remember the source of the first visit – see this GA4 help article).

However, it does not automatically mean that GA4 will "remember" user data that long.

In addition to the cookie expiration, we also have to deal with the GA4 data retention period. It is set by default to only two months, but you can (and basically, you should) change this setting to 14 months. (In the paid version, Google Analytics 360, it can be up to 50 months.)

After this time, Google deletes user-level data from Analytics servers. To keep this data, you must export it to BigQuery (see this GA4 help article).

It means that reports in the Explore section can only be made within the data retention period (please note that in the Explore section, you cannot select a date range beyond this period).

These restrictions do not apply to standard reports in the Reports section that use aggregated data. GA4 will store this data "forever." 

In the unpaid version of GA4, the first user source data are deleted after 14 months of inactivity. After that, this user will be recorded as a new user.

Therefore, there is no point in, for example, changing the cookie expiration time from default 24 months to a longer period, unless you use Google Analytics 360. 

Conversion export to Google Ads

Exporting conversions to Google Ads is often used as an alternative to the native Google Ads conversion tracking, as the fastest and most convenient way to implement conversion tracking in Google Ads.

However, this time-saving seems illusory in the era of Google Tag Manager. Moreover, this solution has many disadvantages. 

There are several arguments against using imported conversions from Google Analytics to optimize Google Ads. It:

  • Reduces the number of conversions observed in Google Ads.
  • Uses exotic attribution.
  • Is vulnerable to unforeseen Google Analytics configuration and link tagging errors, such as unwanted referrals or redundant UTM parameters.

Therefore, while importing conversions from Analytics may provide interesting data that cannot be collected in Google Ads, using them as goals for optimizing Google Ads campaigns may not be optimal. 

If you import conversions from GA4 to Google Ads, regardless of the GA4 attribution settings, the conversions will be imported using the GA4 last non-direct click model.

This means you will only import conversions whose Google Ads source has not been overwritten by subsequent clicks (e.g., organic search results or social media ads).

Regardless of the property-level attribution settings, Google Analytics allows comparisons of different attribution models in the Advertising section.

Currently, the available models are the same as those available in the property settings, and it is impossible to create custom models. 

Interestingly, GA4 allows reporting in two conversion attribution time methods – interaction time and conversion time (only the latter option was available in Universal Analytics).

The interaction time method is typical for advertising systems, where conversions are attributed to clicks and, thus – costs. It allows a correct match between costs and revenues.

Otherwise, the reports might include conversions after the end of the campaign, in a period when there is no ad spend.

On the other hand, the interaction time method may cause the total number of conversions to change depending on the attribution model, as different models may attribute conversions or their fractions to clicks outside the reporting period.

Moreover, the conversion count and revenue for a given reporting period may grow over time until the lookback window closes.

In other words, we may observe more conversions for the recent period if we look at the same report in the future – which is not the case when conversions are reported in the conversion time.

Both approaches have advantages and disadvantages, so it is good that we can now use both.

Conversion paths report

Compared to Universal Analytics, the GA4 conversion paths report is enriched with additional data: time to conversion and the number of interactions for a given path.

It partly compensates for the lack of time lag and path length reports, which were separate reports in Universal Analytics.

The ability to choose an attribution model for this report may be surprising at first sight.

The attribution model does not affect conversion paths. They remain the same, and their length and time to conversion do not change.

In GA4, the path visualization also includes the fraction of conversion assigned to a given interaction or their series in the selected attribution model.

In the last click model, the last interaction always has a 100% share in the conversion, but in the other models, the distribution will be different.

This feature also allows a better understanding of how the data-driven model worked for the interactions in this report. 

Additional bar graphs are placed above the funnel report, visualizing how the selected attribution model assigned a value to channels at the beginning, middle and end of the funnel.

The early touchpoints are the first 25% of the interactions along the path, while the late touchpoints include the last 25%. The middle touchpoints are the remaining 50% of the interactions. 

If you feel that the distribution between early, middle, and late touchpoints does not look as expected for the multi-touch models, please note that if there are only two interactions, there is one early, one late, and no middle interactions.

If there is only one interaction, for the multi-touch models, it will be reported as late interaction – which distorts these reports the most. 

Probably, it would be better if the only interaction was considered as 33.3% early, 33.3% middle, and 33.3% late interaction.

Thus, the attribution model will only affect the bar charts at the top of the report and the percentages shown in the funnel visualization.

The table figures (funnel interactions, conversions, revenue, funnel length, and time to conversion) will remain the same, regardless of the attribution model.

By default, the conversion paths and model comparison reports include all conversions in the GA4 property. Therefore, it is worth remembering to select the desired conversion first. 

Use of scopes in the reports

Again, the source dimensions in GA4 can have one of three scopes: session, user, and event.

  • In the case of the event scope, the attribution model specified in the property attribution settings is used.
  • The session source (session scope) is assigned to the last non-direct interaction at the session start and remains unchanged for a given session, even if there is a visit from another source during the session. It's the "first source" of the session, although assigned in the last-click model.
  • Similarly, the first user source (user scope) is assigned to the last non-direct interaction before the first visit and remains unchanged.

In Google Analytics, all dimensions and metrics operate within their own scope. For example, the Landing page dimension has the session scope, and the Page dimension has the event scope.

Although technically possible, using dimensions and metrics of different scopes can sometimes lead to confusing or difficult-to-interpret reports.

For example, the Page dimension should be matched with Page views, not Sessions. If we combine Pages with Sessions, Universal Analytics will show the number of sessions similar to Landing page vs. Sessions report.

In GA4, this will be the number of sessions during which a given Page has been visited, and therefore, the sum of sessions for all Pages will be greater than the total number of Sessions.

But if you think about it, there is little point in making such reports – therefore, the uncertain interpretation of these numbers should not worry us too much. 

However, some reports using dimensions and metrics of different scopes will make sense. For example, for source dimensions in GA4:

  • The number of events (event scope) paired with the First user source dimension (user scope) shows how many events were generated by users whose first visit was from a given source.
  • The number of events (event scope) paired with the session source dimension (session scope) shows how many events were generated by users during sessions with a given source.

The GA4 documentation fails to indicate how to interpret the number of sessions or users matched with the event scope. Such explorations, although possible, often contain many not set values.

However, creating such reports doesn't make sense. (See the previously mentioned GA4 help article on scopes.)

Modeled data

Finally, it is worth emphasizing the fundamental change in Google Analytics 4, where reports include data collected by the tracking code enriched with modeled data.

The modeled data uses information collected in the cookieless consent mode for users who have not given consent to tracking and Google Signals data for users logged in to Google. This data is fragmentary, but Google can fill in the missing data using extrapolations and mathematical modeling.

Thanks to Google Signals, in GA4, we can see an approximate but more complete picture of the user's journey.

For example, Universal Analytics recorded an iPhone user who visited the website from a YouTube ad using Safari and never returned.

Universal Analytics also saw a conversion made by another user who came from a direct visit on the Chrome browser for Windows.

Google knows these events belong to the same user because this user was logged into Gmail and YouTube. 

This is how Google Analytics 4, using Signals, can model the cross-device users' behavior. It makes the reported number of users more real (reduces it) and improves the attribution accuracy.

In the example above, the conversion from the direct session can be correctly attributed to the YouTube ad.

Not all users are always logged into Google – many do not even have a Google account.

Therefore, to make the picture more complete, Google Analytics will assume that users who are not logged in behave similarly.

Consequently, GA4 sometimes will supplement the missing sources (e.g., assign certain sources to conversions that were previously assigned to direct).

The behavior of users who have not given consent to tracking is estimated similarly.

Analytics knows the number of page views and conversions from the non-consented users and can model how many users generated these pageviews and conservatively attribute conversions to sources.

Enriching Analytics data with Google Signals may take up to a week. Therefore, the recent data may change in the future.

Please note that we also dealt with delays in Universal Analytics, where most reports could have delays of up to 48 hours.

Various privacy-oriented technology solutions, such as PCM by Apple or similar solutions proposed by Google (the Privacy Sandbox), randomly delay conversion reporting by 24-48 hours.

Therefore, we must get used to the fact that the full view of analytical data will only be available after some time. 

In GA4, we can also enhance the reports using the 1st party data, namely the User-ID.

This feature was also available in Universal Analytics, but the separate "User-ID View" included the "logged-in" sessions with User-ID solely and, honestly, wasn't that useful.

GA4 reports combine the User-ID data with the Client-ID (the Analytics cookie identifier) and Google Signals, which makes the data more complete, especially in the cross-device aspect and LTV measurement. 

The complexity of these processes may cause greater or lesser discrepancies between the data in different reports.

We should get used to it, but hopefully, as GA4 recovers from childhood illnesses, these discrepancies will become less and less significant.

It is worth remembering that Google Analytics is not accounting software.

Its objective is not to record every event with 100% precision but to indicate trends and support decision-making – for which approximate data is sufficient.

Author's note: This article was written using Google help articles, answers given by Analytics support and results from my experiments. 

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

New on Search Engine Land

About The Author

Founder and CEO of Adequate Interactive Boutique, awards-winning marketing consultancy. Certified Google Ads and Analytics specialist since 2007. Author of numerous publications, conference speaker, and university lecturer. Expert in measurement, attribution, and profit-driven media optimization.

Wed, 12 Oct 2022 06:53:00 -0500 Witold Wrodarczyk en text/html https://searchengineland.com/google-analytics-4-attribution-guide-388626
Killexams : Tech Trends: The Integrator’s Guide to Cloud Video

This article originally appeared in the October 2022 issue of Security Business magazine. When sharing, don’t forget to mention Security Business magazine on LinkedIn and @SecBusinessMag on Twitter.

There probably is not a person in the security industry today that has not heard the term cloud – just look at the cover of this magazine! That said, hearing and memorizing about it vs. doing it are very different. Part of that relies on education. There are many in our industry who do not know what the cloud is, or how to sell it, or deploy it. Those who do are probably early adopters of most technologies.

Why does cloud adoption matter? The simple reason is that corporate IT departments have been doing this for a while and are providing internal regulation to companies to move to the cloud. It is based around subscription services are counted per head in the company. Microsoft 365 costs $12 per head, Adobe costs $20, and so on. The spend is quantifiable.

Security departments now are faced with transitioning video from an on-premises capital expenditure to a subscription-based model. No longer is video surveillance being sold only by the security integrator – now but droves of IT vendors have jumped into the VSaaS market because they have been selling cloud IoT sensors for years. After all, a camera is just an optical IoT sensor.

The cloud is just a server in another company’s datacenter – with the appeal to lease the space at a fraction of a cost of owning while paying for it out of an operational budget. Security continues to fall under IT more each day, requiring compliance to both company IT architecture as well as purchasing requirements.

VSaaS solutions are fairly new to an already mature IT market; thus, it requires integrators know what type of VSaaS solution will be successful for the end-user.

VSaaS: Is it Cloud-Hosted or Cloud-Managed?

Video Surveillance as a Service or VSaaS is an umbrella term for all cloud video surveillance. At this point, every Video Management System (VMS) manufacturer either has a cloud offering or has it on the roadmap to deliver within the next 18 months. The majority of the services are offered in Microsoft Azure, Amazon Web Services (AWS), Google Cloud, or Wasabi.

VSaaS consists of two primary types: cloud-hosted and cloud-managed. Both offer hybrid solutions that involve a version of onsite recording and the cloud.

Cloud-hosted video involves edge cameras or edge devices recording directly to the cloud via a network connection (wired, wireless or cellular). Most cloud-hosted providers have their own brand of cameras that will record directly to the cloud, or a gateway device – with or without onsite storage – to take traditional IP cameras and transmit them to the cloud.

If your customer has been mandated to go to cloud, cloud-hosted video is a good solution to avoid having to rip and replace the entire camera system. That said, cloud-hosted video has limitations –primarily bandwidth consumption and cost.

Internal camera networks can be segmented and run on their own fiber network, but once that video leaves the site, it has to travel securely across leased lines that carry all inbound and outbound data from the company. This can include traditional web traffic, but also Point of Sale (POS). Too much bandwidth usage can cause latency in other systems, which can be hazardous to business.

Cloud-hosted costs are similar to server and service level agreements (SLAs) broken up over a period of years. To avoid significant cloud costs, most of the cloud-hosted providers have limitations on resolution, frame rate, and retention in the cloud.

Cloud-managed video is edge recording – either to an edge server or to a surveillance-grade SD card in the camera, and then managed through a cloud interface. These edge cameras or servers record everything on the device, and typically have a video analytics package built-in to provide intrusion or object detection video analysis (sending only the analyzed video to the cloud).

Similar to cloud-hosted, cameras provided by the cloud-managed manufacturer are typically cloud-enabled and do not require any additional onsite hardware, while cloud-enabled servers with an analytics package store traditional IP video at the edge, and send only the analyzed video to the cloud. Video sent to the cloud is managed through a central VMS, but each connected device is recording local.

Cloud-managed video typically has a set amount of cloud storage for bookmarked or saved video. Since cloud-managed video is recorded at the edge, each camera can be set up with different variables, with analyzed video receiving a higher resolution and frame rate than non-analyzed video. There is a trade-off between edge hardware and cloud storage costs to be considered, based on the end-user’s requirements and budget.

One thing that regularly gets overlooked with edge recording to surveillance-grade SD cards is that flash recording is not designed for continuous streaming video. Any camera solution using SD cards, no matter what grade, should include replacement of the SD card (if possible) once every year to prevent video retention failure.

Regulatory & Cybersecurity Concerns

Today, almost every integrator/end-user conversation turns to VSaaS. While that does not mean that every end-user is ready for a cloud deployment, as an integrator, it is critical to know the end-user’s use-case and regulatory demands.

For example, cities require video to be Criminal Justice Information Services (CJIS) compliant. Regulated by the FBI, all video used by public safety entities must meet CJIS compliance to be used in court. To meet this requirement, video must be controlled – easy for on-premise solutions, not so easy for cloud. There are very few clouds that comply with CJIS, and even fewer CJIS-compliant VSaaS solutions. Why does this matter if you are not selling to public entities? Well, many corporate security teams came from the public sector, and will require this level of confidence.

Additionally, as more cloud solutions come available, adoption is going to continue. In the last 18 months, AWS has changed their stance from not being CJIS compliant to helping cloud providers hosting in AWS to be CJIS compliant.

Another obvious concern is cybersecurity. While most cloud providers have statements and third-party penetration testing performed, it is important that the VSaaS solution be able to provide its own cybersecurity policies and adherence to best practices. Most VSaaS providers have some version of this, but not all are the same.

Jon Polly is the Chief Solutions Officer for ProTecht Solutions Partners www.protechtsolutionspartners.com, a security consulting company focused on smart city surveillance. Connect with him on linkedin: www.linkedin.com/in/jonpolly.

Wed, 12 Oct 2022 08:19:00 -0500 en text/html https://www.securityinfowatch.com/video-surveillance/hosted-managed-video-surveillance/article/21281737/tech-trends-the-integrators-guide-to-cloud-video
Killexams : Adobe Digital Price Index: Online Prices Fall 0.2% in September
  • Online prices fell 0.2% on an annual basis in September, while rising 0.8% month-over-month

  • Electronics and computer prices fell sharply, along with modest price decreases in toys and sporting goods

  • Grocery prices hit another record high, while pet products and tools/home improvement prices remained elevated

SAN JOSE, Calif., October 12, 2022--(BUSINESS WIRE)--Today, Adobe (Nasdaq:ADBE) announced the latest online inflation data from the Adobe Digital Price Index (DPI), powered by Adobe Analytics. In September 2022, online prices fell 0.2% year-over-year (YoY) while rising 0.8% month-over-month (MoM). In the month prior (Aug. 2022), online prices increased 0.4% YoY. In July 2022, e-commerce had entered deflation for the first time after 25 consecutive months of rising prices, dropping 1% YoY.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20221012005460/en/

Price Table (Graphic: Business Wire)

Prices for electronics, the largest category in e-commerce with 18.6% share of spend in 2021, fell sharply and decreased 11.3% YoY (down 1.2% MoM). This is a greater YoY decrease than August (down 10% YoY) and July (down 9.3% YoY). Prices for computers also fell significantly and decreased 14.1% YoY (down 1.9% MoM), greater than the month prior when prices fell 12.6% YoY. The September price decreases for electronics and computers are both record lows in 2022.

Prices for food have remained high, with grocery prices rising 14.3% YoY (up 0.8% MoM), another record YoY high and the largest increase of any category. Prices for pet products also remain elevated, increasing 11.8% YoY (up 0.01% MoM). Other categories that saw prices jump in September include tools/home improvement (up 10.5% YoY, up 0.3% MoM) and apparel (up 4.7% YoY, up 6.6% MoM)—another major category in e-commerce, second only to electronics.

The DPI provides the most comprehensive view into how much consumers pay for goods online, as e-commerce expands to new categories and as brands focus on making the digital economy personal. Powered by Adobe Analytics, it analyzes one trillion visits to retail sites and over 100 million SKUs across 18 product categories: electronics, apparel, appliances, books, toys, computers, groceries, furniture/bedding, tools/home improvement, home/garden, pet products, jewelry, medical equipment/supplies, sporting goods, personal care products, flowers/related gifts, non-prescription drugs and office supplies.

In September, 11 of the 18 categories tracked by the DPI saw YoY price increases, with groceries rising the most. Price drops were observed in seven categories: electronics, jewelry, books, toys, flowers/related gifts, computers and sporting goods.

Eight of the 18 categories in the DPI saw price increases MoM. Price drops were observed across ten categories including electronics, personal care products, jewelry, books, furniture/bedding, toys, home/garden, appliances, computers and sporting goods.

Notable Categories in the Adobe Digital Price Index for September:

  • Electronics: Prices were down 11.3% YoY (down 1.2% MoM), falling faster than pre-pandemic levels when electronic prices fell 9.1% YoY on average between 2015 and 2019. Prices have fallen consistently since Dec. 2021 (down 2.6% YoY) and accelerated in recent months (down 10% YoY in August, down 9.3% YoY in July).

  • Computers: Prices were down 14.1% YoY (down 1.9% MoM), the biggest drop since the beginning of the COVID-19 pandemic in March 2020. Computer prices have fallen online for 21 consecutive months, and now outpace pre-pandemic levels when prices fell 9.2% on average between 2015 and 2019.

  • Groceries: Prices continued to surge and rose 14.3% YoY (up 0.8% MoM), more than any other category. It is a new record on an annual basis, following a series of record highs: 14.1% YoY increase in August, 13.4% YoY increase in July, 12.4% YoY increase in June. Grocery prices have risen for 32 consecutive months, and it remains the only category to move in lockstep with the Consumer Price Index on a long-term basis.

  • Pet Products: Prices were up 11.8% YoY (up 0.01% MoM), slightly below the record YoY high in the month prior (up 12.7% YoY in August). Online inflation for pet products has now been observed for 29 consecutive months, as pet ownership surged during the COVID-19 pandemic and demand for related goods remains high.

Methodology

The DPI is modeled after the Consumer Price Index (CPI), published by the U.S. Bureau of Labor Statistics and uses the Fisher Price Index to track online prices. The Fisher Price Index uses quantities of matched products purchased in the current period (month) and a previous period (previous month) to calculate the price changes by category. Adobe’s analysis is weighted by the real quantities of the products purchased in the two adjacent months.

Powered by Adobe Analytics, Adobe uses a combination of Adobe Sensei, Adobe’s AI and machine learning framework, and manual effort to segment the products into the categories defined by the CPI manual. The methodology was first developed alongside renowned economists Austan Goolsbee and Pete Klenow.

Adobe Analytics is part of Adobe Experience Cloud, which over 85% of the top 100 internet retailers in the U.S.* rely upon to deliver, measure and personalize shopping experiences online.

About Adobe

Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.

*Per the Digital Commerce 360 Top 500 report (2021)

© 2022 Adobe. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe in the United States and/or other countries. All other trademarks are the property of their respective owners.

View source version on businesswire.com: https://www.businesswire.com/news/home/20221012005460/en/

Contacts

Public relations contacts
Kevin Fu
Adobe
kfu@adobe.com

Bassil Elkadi
Adobe
belkadi@adobe.com

Wed, 12 Oct 2022 00:00:00 -0500 en-US text/html https://www.yahoo.com/lifestyle/adobe-digital-price-index-online-120000971.html
Killexams : Big Discounts Expected For Holiday Shopping Killexams : Experts From Adobe Analytics Say Consumers Can Expect A Big Increase In Discounts For This Year's Holiday Shopping Season - CBS News Live Video - CBS Philadelphia

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Mon, 10 Oct 2022 15:09:00 -0500 en-US text/html https://www.cbsnews.com/philadelphia/live/video/20221011015023-experts-from-adobe-analytics-say-consumers-can-expect-a-big-increase-in-discounts-for-this-years-holiday-shopping-season/
Killexams : A Marketer’s Guide To TikTok Analytics

While TikTok might be all fun and games for users, marketers know better.

This social media channel is full of essential metrics to help brands create more engaging and relevant content for their audiences, so let’s get right to it: TikTok analytics.

Read below to learn what you’re looking for on TikTok’s analytics platform, how to interpret the data you find, and how to use that data to grow your brand’s presence.

Accessing Your TikTok Analytics

The first thing to know is that you must have a business or an influencer account to access TikTok analytics rather than a standard account.

Make the switch to a professional account by:

Tap the Hamburger icon (upper-right)> Settings & Privacy > Manage Account > Switch to a Business Account > Select your business category.

Once you’re set, you can navigate to your analytics by:

Tap the Hamburger (upper-right) > Click Creator Tools > Click Analytics.

Reviewing TikTok Analytics Data

When you navigate your analytics page, you will see three different categories at the top of your screen that you can click into for more data: Overview, Content, and Followers.

Overview

Like other social analytics platforms, TikTok offers a snapshot of how your content has been performing over a select period and the % increase or decrease since the previous period (shown in blue).

This data includes two significant aspects of your channel:

  • Video views: Shown in a graphical format, you can easily pick out trends over the last month on your channel regarding how many people watched your videos.
  • Profile views: Here, you will see the number of likes, comments, and shares for each video you’ve posted over a given period.

Additional metrics offered under the Overview tab include:

  • Likes: The number of likes your videos received in the selected date range.
  • Comments: The number of comments your videos received in the selected date range.
  • Shares: The number of shares your videos received in the selected date range.
  • Followers: The total number of users that follow your account and how that has changed within the selected date range.
  • Content: The number of videos you have shared in the selected date range.

Followers

When you scroll down on that same overview page, you’ll next see that you can click into your “followers” analytics (also found as a tab at the top of your screen, as shown above).

Here you’ll be able to see:

  • How many followers your account has.
  • Your follower growth percentage since the previous period you selected.
  • Demographic information about your followers, such as gender and location.
  • The hours your followers are most active.
  • The days your followers are most active.
  • The sounds your followers have listened to.

Although the actual analytics page might not look like much, we consider this the essential metric TikTok offers (more on this later).

Content

Your analytics page’s “content” tab is a great way to see which content you post is getting the most attention.

You will see this tab broken up into several subsections showing data from the last seven days, including:

  • Video posts: Here, you can see the last nine videos you posted and determine which video had the highest number of views.
  • Trending videos: The videos with the fastest growth rate in views over the last week are presented here.
  • Video views by section: Here, you can see if people found your video via your profile, someone else’s profile, or if you appeared in their feed.
  • Video views by region: This helps you understand where your content is resonating geographically.
  • Average watch time: We love this metric because it helps show you what’s engaging!
  • Total playtime: Unlike average watch time per video, your total playtime shows you a cumulative watch time for anyone who has watched your videos.

Note: If you have more than 1,000 TikTok followers, you are eligible to host Live TikTok videos. With Live videos comes another analytics page for you to see precisely how your live video performed. Learn how to get started with a live TikTok video here.

Extra: Total Engagement Rates

SocialChamp offers a little hack to help you get another metric that could be useful: total engagement rates.

Just follow one of the below formulas to calculate this number:

Monitoring TikTok Hashtags

Metrics surrounding hashtags aren’t found in the same place as the metrics discussed above. However, it’s still worth analyzing different hashtags in your niche and seeing the number of times a post with a particular hashtag has been viewed.

To find this data, use the Search bar to find a hashtag.

Here, you’ll be able to see the number of views that hashtag has, the top videos that use the hashtag, and related hashtags.

Using TikTok Analytics To Grow Your Channel

Each section of TikTok analytics provides you with the valuable insight needed to grow your page and influence the right audience. Here are the three main takeaways:

Know The Ideal Times To Post Content

This is low-hanging fruit. See when (the day and time) your followers are engaging on the platform, and that’s when you should aim to post.

Understand The Videos That People Like The Best

Look at which videos people engage with the most through likes, comments, and watch time.

If you find that your funny videos outperform your informative ones, you know what to do!

These metrics should help guide your content creation strategy.

Discover What Is Working With Your Audience Beyond Your Page

Look at the analytics for popular sounds your audience likes and what hashtags they are using, and start incorporating these into your future videos.

Final Thoughts

Love it or hate it, TikTok isn’t going anywhere anytime soon.

Even if your audience isn’t on TikTok yet, we suspect more and more will adopt this platform, just as they eventually did with Instagram.

So, getting a head start on understanding how it works and creating a channel that resonates with and engages your target audience is best. (Remember, you can always repurpose your TikTok content for platforms like Instagram and Facebook!).

Happy analyzing!

More resources:


Featured Image: Petryshak/Shutterstock

Thu, 29 Sep 2022 02:00:00 -0500 en text/html https://www.searchenginejournal.com/marketers-guide-tiktok-analytics/464180/
Killexams : The LAMag Guide to Weekend Sales: A.P.C., Fillyboo, Proezna Schouler

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Fri, 14 Oct 2022 17:44:00 -0500 en-US text/html https://www.lamag.com/lalifeandstyle/the-lamag-guide-to-weekend-sales-a-p-c-fillyboo-proezna-schouler-and-more/
Killexams : Adobe Digital Price Index: Online Prices Fall 0.2% in September
  • Online prices fell 0.2% on an annual basis in September, while rising 0.8% month-over-month

  • Electronics and computer prices fell sharply, along with modest price decreases in toys and sporting goods

  • Grocery prices hit another record high, while pet products and tools/home improvement prices remained elevated

SAN JOSE, Calif., October 12, 2022--(BUSINESS WIRE)--Today, Adobe (Nasdaq:ADBE) announced the latest online inflation data from the Adobe Digital Price Index (DPI), powered by Adobe Analytics. In September 2022, online prices fell 0.2% year-over-year (YoY) while rising 0.8% month-over-month (MoM). In the month prior (Aug. 2022), online prices increased 0.4% YoY. In July 2022, e-commerce had entered deflation for the first time after 25 consecutive months of rising prices, dropping 1% YoY.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20221012005460/en/

Price Table (Graphic: Business Wire)

Prices for electronics, the largest category in e-commerce with 18.6% share of spend in 2021, fell sharply and decreased 11.3% YoY (down 1.2% MoM). This is a greater YoY decrease than August (down 10% YoY) and July (down 9.3% YoY). Prices for computers also fell significantly and decreased 14.1% YoY (down 1.9% MoM), greater than the month prior when prices fell 12.6% YoY. The September price decreases for electronics and computers are both record lows in 2022.

Prices for food have remained high, with grocery prices rising 14.3% YoY (up 0.8% MoM), another record YoY high and the largest increase of any category. Prices for pet products also remain elevated, increasing 11.8% YoY (up 0.01% MoM). Other categories that saw prices jump in September include tools/home improvement (up 10.5% YoY, up 0.3% MoM) and apparel (up 4.7% YoY, up 6.6% MoM)—another major category in e-commerce, second only to electronics.

The DPI provides the most comprehensive view into how much consumers pay for goods online, as e-commerce expands to new categories and as brands focus on making the digital economy personal. Powered by Adobe Analytics, it analyzes one trillion visits to retail sites and over 100 million SKUs across 18 product categories: electronics, apparel, appliances, books, toys, computers, groceries, furniture/bedding, tools/home improvement, home/garden, pet products, jewelry, medical equipment/supplies, sporting goods, personal care products, flowers/related gifts, non-prescription drugs and office supplies.

In September, 11 of the 18 categories tracked by the DPI saw YoY price increases, with groceries rising the most. Price drops were observed in seven categories: electronics, jewelry, books, toys, flowers/related gifts, computers and sporting goods.

Eight of the 18 categories in the DPI saw price increases MoM. Price drops were observed across ten categories including electronics, personal care products, jewelry, books, furniture/bedding, toys, home/garden, appliances, computers and sporting goods.

Notable Categories in the Adobe Digital Price Index for September:

  • Electronics: Prices were down 11.3% YoY (down 1.2% MoM), falling faster than pre-pandemic levels when electronic prices fell 9.1% YoY on average between 2015 and 2019. Prices have fallen consistently since Dec. 2021 (down 2.6% YoY) and accelerated in recent months (down 10% YoY in August, down 9.3% YoY in July).

  • Computers: Prices were down 14.1% YoY (down 1.9% MoM), the biggest drop since the beginning of the COVID-19 pandemic in March 2020. Computer prices have fallen online for 21 consecutive months, and now outpace pre-pandemic levels when prices fell 9.2% on average between 2015 and 2019.

  • Groceries: Prices continued to surge and rose 14.3% YoY (up 0.8% MoM), more than any other category. It is a new record on an annual basis, following a series of record highs: 14.1% YoY increase in August, 13.4% YoY increase in July, 12.4% YoY increase in June. Grocery prices have risen for 32 consecutive months, and it remains the only category to move in lockstep with the Consumer Price Index on a long-term basis.

  • Pet Products: Prices were up 11.8% YoY (up 0.01% MoM), slightly below the record YoY high in the month prior (up 12.7% YoY in August). Online inflation for pet products has now been observed for 29 consecutive months, as pet ownership surged during the COVID-19 pandemic and demand for related goods remains high.

Methodology

The DPI is modeled after the Consumer Price Index (CPI), published by the U.S. Bureau of Labor Statistics and uses the Fisher Price Index to track online prices. The Fisher Price Index uses quantities of matched products purchased in the current period (month) and a previous period (previous month) to calculate the price changes by category. Adobe’s analysis is weighted by the real quantities of the products purchased in the two adjacent months.

Powered by Adobe Analytics, Adobe uses a combination of Adobe Sensei, Adobe’s AI and machine learning framework, and manual effort to segment the products into the categories defined by the CPI manual. The methodology was first developed alongside renowned economists Austan Goolsbee and Pete Klenow.

Adobe Analytics is part of Adobe Experience Cloud, which over 85% of the top 100 internet retailers in the U.S.* rely upon to deliver, measure and personalize shopping experiences online.

About Adobe

Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.

*Per the Digital Commerce 360 Top 500 report (2021)

© 2022 Adobe. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe in the United States and/or other countries. All other trademarks are the property of their respective owners.

View source version on businesswire.com: https://www.businesswire.com/news/home/20221012005460/en/

Contacts

Public relations contacts
Kevin Fu
Adobe
kfu@adobe.com

Bassil Elkadi
Adobe
belkadi@adobe.com

Wed, 12 Oct 2022 08:30:00 -0500 en-US text/html https://finance.yahoo.com/news/adobe-digital-price-index-online-120000971.html
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