The MarketWatch News Department was not involved in the creation of this content.

Feb 15, 2023 (The Expresswire) -- As per Market Growth Report, “The Digital Marketing Analytics market has witnessed a growth from Multimillion USD to Multimillion USD from 2017 to 2022. With a Impressive CAGR, this market is estimated to reach Multimillion USD in 2029.”

DigitalMarket is expected to reach multi million by 2029, in evaluation to 2022, Over the few years the Digital Market will reach a magnificent spike in CAGR in terms of revenue. Report is spread across 128 Pages and provides exclusive data, information, vital statistics, trends, and competitive landscape details in this niche sector.

The MarketWatch News Department was not involved in the creation of this content.

This post was sponsored by iQuanti. The opinions expressed in this article are the sponsor’s own.

What transforms a website from just a catalog of blog posts to a highly-organized hub of authoritative content?

How can you make sure your website becomes the top-visited resource for your industry?

Today, we’re going to travel down the content rabbit hole, and peer through the SEO looking glass, if you will.

You’ll discover how to easily merge content creation and SEO to help your website successfully rank at the top of search engine results pages (SERPs) using subject clusters.

Your goal: Create content that is able to answer your target audience’s questions by providing everything they need to know about their search queries.

The classic Alice in Wonderland series posed a quasi-metaphorical question to its protagonist: “Why is a raven like a writing desk?”

The question posited has nothing to do with the plot, leaves nothing to inform the characters’ growth or intentions, and may very well be construed as pure nonsense, a symptom of fantasy.

At the end of the story, Alice asks The Hatter, “Why is a raven like a writing desk?”

Though no answer was provided – and many answers have been theorized since – readers not only embrace this fantastical reality but champion it as part of the inherent logic of the narrative.

We’ve come to Wonderland to believe in the absurd possibilities of this reality, however nonsensical they may appear.

When it comes to your SEO strategy, you won’t be delving into fictional worlds or constructing outlandish characters and situations. You will, however, be in command of your narrative and its inherent logic.

You will need to:

• Understand what your audience is looking for or trying to understand, lest you lose out on active users’ time on your page – a critical metric in search engine algorithms.
• Know how your content is interconnected, how to prioritize it, and how to measure its impact.
• Remember why it’s essential to keep your customer following the narrative down the content rabbit hole.

Step 1: Understand & Track Search Intent

Your audience’s search history and webpage behavior can speak volumes about their search intent and needs, even if it’s impossible to actually know what’s going on in a person’s mind.

SEO’s analytical aspects can uncover search intent based on keyword queries.

If you pair SEO analytics with subject-matter experts (SMEs), you can also supercharge your strategy by plotting topical interests and predicting a person’s position within your marketing funnel.

How To Discover Search Intent To Build Successful subject Clusters

You and your SMEs can easily develop one or more themed keyword landscapes, or “topic clusters”, by simply researching the root of a regular person’s keyword queries:

1. Define query intent across your customer journey. Are your customers more likely to use search terms that signal awareness, consideration, comparison, or purchase intent in different parts of your sales and marketing funnel?
2. Audit your existing keyword footprint, your competitive footprint, and your pay-per-click (PPC) performance.
3. Explore and document new keyword opportunities that logically arise from your audit.
4. Compile a master keyword list.

Your master keyword list will help you and your team clearly understand how your points of entry can be better positioned or supported on a user’s journey through your sales funnel.

Step 2: Create A Master Keyword List To Uncover Keyword Gaps & Cluster Foundations

You’ll want to walk away from step one with a master keyword list that will help you discover the content gaps that can be used to bridge your past content efforts with your present or future ones.

This helps you quickly see if your target audience’s search intent matches your website’s page intent.

Why A Master Keyword List Works

Master keyword lists work because they help make sure you’re covering every need of your target audience.

Once you’ve completed your master keyword list, look at how complete and logical your list of targeted keywords truly is.

Ask yourself if a reader finds your website after a query:

• Will they need to take a step back to figure out how believable or valid the information you’re presenting them is?
• Will the provided backlinks on that page assume that this user knows all they need to know, while attempting to redirect them to a product page without informing them of how you perform better than your competitors?

Either of these paths can quickly become disjointed without a logical flow of information.

That’s where the last high-level takeaway from your master keyword list can be beneficial: spinning a web of core, supporting, and related themes to ensnare and enthrall your user.

This is what creates the foundation of subject clusters.

Step 3: Create Content & subject Clusters

As you’ve seen, search intent needs to translate to page intent.

So, careful consideration during the keyword selection phase is key for content creation because of the many themes and subthemes that your brand, products, and services may cover.

These subthemes are the pillars of your topic cluster strategy. Implementing content clusters is an effective means of covering full courses in organized, digestible bites.

How To Create Content Clusters

Once your audience stumbles upon your landing page, the content must be compelling enough to draw their attention further.

Engage perceptions with stats and diagrams, provide visuals and lists, condense information, and challenge inquisitive minds to learn more on the next page.

Here’s how:

1. Select your highest priority acquisition – conversion – pages and exhaust the themes and subthemes relating to it by building relevant awareness and consideration articles around it.
2. Identify and connect the journey to different courses and subthemes with backlinks. For example, let’s say you’re building a product page for personal loans. You will naturally need to mention loan rates and credit scores to substantiate your offering.
3. Create spinoff content. For example, can this spin off into article pages for “What’s a Good Credit Score?” and “How to Qualify for Affordable Loan Rates”? You could even compile information about the loan rates you offer for each credit score tier, compare your loan rates to your competitors, provide tips for improving your credit score, and so many more themes within themes.
4. Enhance your content with videos, infographics, or sound bites.

As readers spin through your website, they become familiar with your brand, style, and voice, and might even develop some level of trust from their visit – if you’ve provided the information they’re looking for.

Step 4: Review Content Marketing Metrics & Analytics

Tracking where your readers explore your site more thoroughly will only further the plot – keeping in mind general consumer demand and some KPIs.

Whether it’s the value added by a narrative or a more consistent website flow, pages can be identified for their:

• Conversion rate (CVR).
• Page views.
• Impressions.
• Time on page.
• And many other relevancy qualifiers concerning their intent.

After identifying successes, you can mimic these pages, which then can be integrated into your audience’s journey.

If metrics stagnate for some pages, they might need a refresh. Exploring different keyword landscapes and bridging cluster gaps can help Boost your site’s authority and user experience (UX) overall.

In the worst-case scenario – after you’ve decided it’s no longer relevant to your site visitors or from a keyword volume standpoint – you can decommission and 301 redirect any content that no longer performs in a way that fulfills your brand’s purposes.

Ensuring your site structure ranks well compared to competitors or industry aggregators can be challenging without sophisticated technological support – and there’s only so much that can be done unless you want to overhaul your website.

However, catching and nesting a customer to a purchase decision should involve an intuitive flow that won’t require the customer to encounter navigational mishaps, such as having to back out of pages.

Create A subject Cluster-Driven World Of Your Own

At the beginning of her journey, freshly lost and before the conversation about a raven and a writing desk, Alice asks the Cheshire Cat, “Would you tell me, please, which way I ought to go from here?”

The Cat replies, “That depends a good deal on where you want to get to.”

Though we’ve all been Alice before – on a website, in a crowd, within a metaphor – you should be fostering the journey with the same absurd seriousness the Cheshire Cat proposes.

In the story, Alice must find herself before knowing what she wants to do.

Your customers may stumble upon you with questions, curiosities, and intent – are you positioning your content in a relatable way?

iQuanti uses the combination of their proprietary tech and trained UX strategists to:

• Identify and close gap deficiencies.
• Track algorithmic score changes.
• Improve site performance from a page level upwards.

If you believe you could use some help identifying content gaps or want to help drive the needle forward for your business, iQuanti’s SEO services could be just what you need.

Descriptive analytics is the most basic form of data analytics. A quick Google search will show that this type is most commonly used to answer the question “what” or “what happened?”

The role of descriptive analytics is to analyze current or past data to uncover trends or anomalies. Often, this method is used to track business metrics and key performance indicators (KPIs) to show progress toward goals.

For example, monthly revenue, number of new customers, number of products sold, and average test score are all types of descriptive data.

Descriptive analytics is just that: a description. Data is analyzed at face value. Other types of data analytics, such as diagnostic analytics, must be used to turn basic data into actionable insights.

For more information, also see: Best Data Analytics Tools 

What are the 3 Key Types of Data Analytics?

While descriptive analytics is the foundational form of data analytics, there are three other key types that are used in many organizations today: diagnostic, predictive, and prescriptive. These three types are often used after descriptive analytics for deeper analysis.

Diagnostic Analytics

Diagnostic analytics is often the next step after descriptive analytics. Diagnostic analytics aims to “diagnose” the data by answering the question “why?”

For example, a descriptive data report may show that revenue is up for the quarter. However, diagnostic analytics goes deeper, analyzing why revenue has increased. Methods used for diagnostic analytics range from statistical analysis to probability.

Predictive Analytics

Predictive analytics takes descriptive and diagnostic analytics a step further by using current and past data to predict the future.

For example, this type of analytics is used often in the manufacturing industry. Machine data is extracted over time and analyzed for trends that may predict future failure. If an anomaly is found, maintenance or repairs can be conducted before unplanned downtime occurs.

Another use case for predictive analytics is risk modeling. For example, businesses can use data to understand various financial risks and mitigate them before they negatively impact operations.

Prescriptive Analytics

Prescriptive analytics uses advanced processes such as machine learning, artificial intelligence (AI), and intelligent algorithms to “prescribe” or recommend an action plan based on data. In some cases, this process involves performing specific actions based on outcomes.

For example, let’s consider our manufacturing use case from above. A manufacturer may uncover a future machine failure during the predictive analytics process. However, more intelligent, prescriptive analytics may uncover that failure is imminent and a shutdown should occur to prevent further damage.

Prescriptive analytics is slowly becoming mainstream as tech such as AI continues to evolve. exact numbers show that the size of the prescriptive analytics market is expected to reach $12.35 billion by 2026.

To learn more, also see: Top Business Intelligence Software 

How Is Descriptive Analytics Processed?

Raw data is rarely ready to yield insights. For example, data may be unstructured, meaning it doesn’t follow a specific data model, making it difficult to analyze. Plus, data can be spread across a multitude of disparate sources, from an organization’s CRM to public databases.

This is where the descriptive analytics process begins. First, data must be wrangled from all of its sources into a single location, such as a data warehouse. This step is completed through processes such as ETL (extract, transform, load) or data virtualization.

Once the data is wrangled, it can then be cleansed and organized. For example, data cleaning involves removing duplicate or incomplete data from a dataset. The cleaning step ensures the data is trustworthy, which is critical when using it to make decisions.

After the data is cleansed, the analysis step can begin. In the past, data would be loaded into spreadsheets and then analyzed for patterns. But now, there are many tools and software platforms that eliminate the need for tedious spreadsheets.

More on this topic: Top Data Virtualization Tools

4 Real-World Examples of Descriptive Analytics

Descriptive analytics is the most common form of data analytics. It’s used across industries, niches, and markets, for everything from determining annual budgets to identifying consumer trends.

Reporting on finances

Descriptive analytics can be used to monitor the financial well-being of any organization. For example, businesses can pull data on everything from monthly revenue to the number of products sold in any given week.

This descriptive data can then help stakeholders form insights and make decisions on future product development, sales goals, asset purchases, and so much more.

Monitoring marketing campaign success

Many organizations use descriptive analytics to check the performance of their marketing campaigns. For example, descriptive data can deliver insights on conversions, new leads, new customers, and marketing spend.

Descriptive analytics can also help you bridge the gap between traditional marketing initiatives and digital marketing. For example, data can pinpoint trends involving social media impressions, website bounce rate, and even clicks on business Facebook ads.

This data is then used to inform future marketing campaigns, which have a direct impact on the success of a business.

Tracking overall business performance

Descriptive data can go even further than tracking finances or marketing campaigns. Data can help you understand the overall performance of your business by uncovering insights such as growth rate, churn rate, and even employee engagement.

This data arms stakeholders with the proof they need to make sound business decisions to keep their organizations moving forward. It also helps pinpoint potential business risks that must be mitigated.

Identifying trends

Perhaps the most common use of descriptive data that crosses all  industries is simply identifying trends.

For example, in manufacturing, machine data can be analyzed to pinpoint ongoing mechanical issues that, if not resolved, could result in serious downtime. And in healthcare, descriptive data can be used to track patient health and Boost care outcomes.

Descriptive Analytics Tools

There are many tools you can use for descriptive analytics. For example, many platforms work to collect data and then deliver insights using interactive dashboards. Instead of trying to glean insights from a spreadsheet, these platforms use intuitive graphs and charts to help you visualize data at a glance.

Some examples of popular descriptive analytics tools include:

• SAP Analytics Cloud
• SAS
• Tableau
• Apache Spark
• Sisense

The Descriptive Analytics Market

According to recent data, 97% of organizations are investing in data initiatives. And the big data market is expected to reach $473.6 billion by 2030. In other words, data is king, and organization can’t move forward without it.

Unfortunately, many organizations are struggling to put data to good use. Another survey found that only 26.5% of companies have successfully created a data-driven organization.

While there are many factors at play here, such as a lack of data talent, some of the growing pains revolve around the complexity of data analytics. After all, there are so many different components, from data types to analytics models.

However, by taking analytics back to the basics, we can see it for what it was originally intended to be: a tool for answering the simple question of “what.” This question can be answered through the foundational data analytics type: descriptive analytics.

For more information, also see: Data Analytics Trends 

Super Bowl LVII — a down-to-the-wire thriller with plenty of storylines, much-anticipated commercials, and Rihanna’s hit parade of a halftime extravaganza — was watched by 113 million people on Sunday evening.

A fantastic number, one of the best in the history of American television. But soccer fans may be inclined to triumphantly flip their scarves and point out that the Super Bowl’s viewership is a fraction of the FIFA men’s World Cup final’s audience.

Thanks to the games’ proximity on the calendar with the World Cup’s one-time move to November-December last year, there may be a natural inclination among some fútbol partisans to boast that the World Cup’s finale draws a vastly bigger global audience at nearly 1.5 billion, which is true. But is it a fair comparison?

The answer is probably not, for myriad reasons.

The NFL has dominated American television for decades, and the Super Bowl culminates pro football’s season as a secular national holiday. The game’s audience remains large, in the era of cord-cutting and people using television less, because of a blend of football’s domestic popularity along with tradition, and casual fans tuning in for the commercial showcase and the half-hour halftime show. There’s something for everyone, even if you don’t know Patrick Mahomes from Patrick Star.

Soccer has been steadily gaining popularity among Americans for decades, both to play and to watch, but remains far behind other sports in terms of U.S. television numbers. And in the case of the World Cup finale, it’s a national-team match with a built-in global audience on hundreds of channels while the Super Bowl is played on one domestic network, and some overseas, by the best of 32 teams based entirely in the continental United States — perhaps the fundamental factors in why they have a vast overall audience differential.

The 113 million figure for the Kansas City Chiefs’ win over the Philadelphia Eagles in Super Bowl LVII is an aggregate number that includes primary broadcaster Fox’s linear television audience along with Spanish-language Fox Deportes, out-of-home viewership, and both Fox and NFL digital platforms, per number-crunching by Nielsen Media Research and Adobe Analytics via Fox Sports. The peak was 118.9 million from 8 to 8:15 p.m.

That audience average ranks as the third-most viewed U.S. television program since tracking began in the early 1950s, which is no surprise since the Super Bowl has been the most-watched single-network broadcast on American TV every year since 1983, when the “MASH” finale was the last non-Super Bowl program to sit atop a year’s viewership.

The NFL, which is making an overseas push, has said the Super Bowl’s “reach” — defined as people who watched a minute or more of the game — outside the United States in exact years is about 40 million while the average audience total is the 113 million figure for domestic viewership.

In the U.S., Argentina’s World Cup final victory over France via penalty kick shootout on Dec. 18 averaged 16.78 million U.S. viewers on Fox and its platforms, and another 9 million on Telemundo and Peacock.

In the context of the current state of the television industry, that combined 26 million viewers for the exact men’s World Cup final is an excellent number, and about on par with an NFL wild card or lesser divisional playoff game.

The largest U.S. TV audience for any single-network soccer match broadcast is 25.4 million on Fox for the 2015 Women’s World Cup final that saw the U.S. beat Japan. It also averaged 1.27 million U.S. viewers on Telemundo, meaning the match’s total U.S. audience average was 26.6 million.

The American audience for this year’s men’s final match contributed a chunk of the global audience that FIFA, the world soccer’s governing body, is boasting about.

Here’s how FIFA formally termed its audience total for the 2023 World Cup championship match: “The final achieved a global reach of close to 1.5 billion viewers (up from 1.12 billion in 2018).”

In the final’s participating home nations, the match had 24 million viewers on French television’s TF1 and 12.07 million across three channels airing the match live in Argentina on TV Publica, TyC Sports, and DirecTV, per FIFA data.

The nearly 1.5 billion in reach is functionally a brag. Having that many people consume some part of the match broadcast is absolutely a good thing, but the real global audience that watched the game under traditional measurements for a bigger chunk of time is a much lower figure, but FIFA hasn’t formally given a specific average. FIFA’s media unit deferred questions about the audiences specifics to the general fact sheet already published and said more numbers will be coming out in a few weeks.

Still, a more precise World Cup audience average would still be more than the Super Bowl, but that’s a function of the final airing on hundreds of channels across the entire planet. While the NFL title game does have overseas broadcast rights, with it being aired in “about 200 countries” with more than a dozen non-U.S. broadcast crews on-site, it just doesn’t have the global popularity of soccer’s quadrennial championship.

Germany’s 1-0 win over Argentina in the Brazilian-hosted 2014 World Cup final has been the gold standard for the game’s TV viewership with an average of 570.1 million watching, based on Kantar Media and FIFA data reported by the Associated Press. The measurements at the time didn’t include mobile-phone or online users.

Using the reach metric, the 2014 match had 1.01 billion people watch at least a minute of the broadcast. About 700 million reportedly watched at least 20 minutes.

It doesn’t help American World Cup viewership that the U.S. men’s national team isn’t a soccer powerhouse like the wildly successful women’s side. But its modest success in the 2022 event helped with U.S. audience numbers overall and is a positive for future audience growth. And it’s certainly a flight of fancy for American fans and U.S. TV execs to dream about the USMNT reaching the final at home in 2026 (the championship game stadium will be selected by FIFA sometime this year).

What makes soccer audience tracking tricky outside the United States are a few factors. One is trusting FIFA and its data — its reputation isn’t the best in sports, to be polite. Another is that some countries that air the World Cup don’t have audience measurement services such as Nielsen and Kantar Media, or use varying tabulation methods, so that data may be absent or suspect.

Soccer-mad Brazil, with a population of 217 million and its own domestic TV metrics company called IBOPE, averaged almost 37 million viewers for the World Cup final, per FIFA data.

Of course, Nielsen data at its core also is highly educated guesswork — viewership extrapolations based on electronic sampling from 42,000 demographic panels from among the nation’s 121 million television households, plus more precise data on tracked digital and streaming consumption.

Even with imperfect viewership measurements, there’s no dispute that the NFL remains emperor of U.S. television, but soccer continues to make incremental gains as a whole.

For 2022-23, the NFL’s 272 regular-season games averaged 16.7 million viewers across its major network and tech partners. While that’s a decline from the 2021 season’s 17.1 million average, the NFL traded a smaller reach for its Thursday night games streamed on Amazon Prime Video in return for a much higher rights fee for those games.

The NFL’s regular-season viewership record is 18.1 million set in 2015.

For soccer aired in the U.S., Comcast/NBC-owned Spanish-language Univision’s most exact Liga MX season averaged 939,000 U.S. viewers for Mexico’s top league, per Sports Video Group reporting.

Britain’s Premier League enjoys the second-best overall U.S. soccer viewership on average, and NBC’s broadcast of Arsenal’s 3-2 win over Manchester United on Jan. 22 averaged 1.92 million viewers (aggregate across NBC, Peacock, digital) to make it the most-watched Premier League match in U.S. television history. It also got 382,000 U.S. viewers on Telemundo.

Premier League matches last season averaged 507,000 U.S. viewers across NBC, USA, CNBC, NBCSN and Peacock, which made for the second-best American viewership season for the league since 514,000 viewers.

Major League Soccer’s 34 matches that aired nationally on ABC and ESPN networks last season averaged 343,000 viewers, which was up 16 percent from 2021 and the best average on the Disney-owned channels since 357,000 in 2007. (MLS goes behind the Apple TV paywall this season.)

Observers have noted that the NFL is home to the best football players in the world, while the best soccer players are in foreign leagues, particularly Europe’s top professional sides — a fact that may suppress additional interest in soccer here until more of the very best are regularly playing and winning on American pitches.

Another factor that often works against the World Cup’s U.S. viewership is the location of the tournament every four years. In 2022, the final in Qatar was eight hours ahead of the U.S. Eastern Time Zone.

The France-Argentina match began at 10 a.m. on a Sunday in the U.S., well ahead of NFL live game coverage but with extra time and the shootout ending moments before NFL games kicked off, the soccer match’s trophy ceremony and postgame analysis was booted to FS1.

Last year’s World Cup was shifted from its normal summertime schedule to the later fall because of Qatar’s brutal heat, a move that put the matches up against college and pro football in the United States — the two biggest viewership draws on American television, but apparently not especially major siphons as many worried.

In fact, this year’s World Cup broadcasts by Fox saw some audience records set.

The 0-0 draw between the United States and England during group-stage play on Nov. 25 averaged 15.38 million viewers on Fox, which set the mark for the biggest U.S. audience for a men’s soccer match on a single network. That mark lasted until the final on Dec. 18.

With Fox’s digital numbers included, the USA-England total leaps to 17.2 million viewers for the Black Friday match. Another 4.6 million watched on Telemundo’s linear and digital platforms.

Those are great numbers for soccer on U.S. television, to be sure, but they came a day after the NFL enjoyed a new regular-season viewership record when the mid-afternoon Giants–Cowboys game broke 42 million viewers on Fox — a reminder that the NFL’s grip on domestic TV remains iron-clad today.

The NFL, which now has a bundle of media rights deals that will pay it north of $113 billion over the next 11 years, is eager to ensure that it has as much Super Bowl viewership as can be measured — particularly amid the changes across the TV industry.

A year ago, the league hired Nielsen to conduct a custom post-Super Bowl audience survey of 6,600 households using the research organization known as NORC at the University of Chicago to determine the size of viewing groups across locations beyond what Nielsen may normally track. Results show an estimated 208 million people watched Super Bowl LVI.

Because the game is the top U.S. television draw and has long lent itself to family and group watch parties, and heavy viewership outside the home at places such as bars and restaurants, the NFL wanted better audience estimates for this unique TV property. Audience translates into dollars, after all, and the league has staked out a goal to be a $25 billion entity by 2027.

The formal addition of out-of-home viewership tracking in 2020 meant Nielsen clients would get more detailed metrics on who is watching where and when. And while OOH remains imperfect — Nielsen audio signals at crowded sports bars showing different games and events isn’t simple to slice and dice — it remains the currency of the U.S. television industry. Using OOH also means older Super Bowls had larger audiences than the known totals.

“While it’s no secret that the Super Bowl is the biggest event across the media landscape on a yearly basis, the exact number of people watching the game has been challenging to pinpoint given the fact that people tend to gather in groups to watch the game,” said Paul Ballew, chief data and analytics officer of the NFL, in a statement about last season’s Super Bowl survey.

What this ultimately means is that the Super Bowl and World Cup are enormously lucrative events even as the television business continues to evolve, and while their respective viewership totals may be fun watercooler boasting topics, they’re ultimately two different entities — an apples-to-oranges comparison, or perhaps footballs to soccer balls. The Super Bowl isn’t going to become the most-watched sporting event on Earth, and the FIFA World Cup — nor anything else — is likely going to oust the NFL title game from its domestic throne.

(Top photo of the Kansas City Chiefs celebrating their win in Super Bowl LVII: Christian Petersen / Getty Images)

Predictive analytics definition

Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future.

Predictive analytics has captured the support of wide range of organizations, with a global market size of $12.49 billion in 2022, according to a research study published by The Insight Partners in August 2022. The report projects the market will reach $38 billion by 2028, growing at a compound annual growth rate (CAGR) of about 20.4% from 2022 to 2028.

Predictive analytics in business

Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning, and assorted mathematical processes. Organizations use predictive analytics to sift through current and historical data to detect trends and forecast events and conditions that should occur at a specific time, based on supplied parameters.

With predictive analytics, organizations can find and exploit patterns contained within data in order to detect risks and opportunities. Models can be designed, for instance, to discover relationships between various behavior factors. Such models enable the assessment of either the promise or risk presented by a particular set of conditions, guiding informed decision-making across various categories of supply chain and procurement events.

For tips on how to effectively harness the power of predictive analytics, see “7 secrets of predictive analytics success.”

Benefits of predictive analytics

Predictive analytics makes looking into the future more accurate and reliable than previous tools. As such it can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules, and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants, and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.

By optimizing marketing campaigns with predictive analytics, organizations can also generate new customer responses or purchases, as well as promote cross-sell opportunities. Predictive models can help businesses attract, retain, and nurture their most valued customers.

Predictive analytics can also be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.

Predictive analytics use cases

Organizations today use predictive analytics in a virtually endless number of ways. The technology helps adopters in fields as diverse as finance, healthcare, retailing, hospitality, pharmaceuticals, automotive, aerospace, and manufacturing.

Here are a few ways organizations are making use of predictive analytics:

• Aerospace: Predict the impact of specific maintenance operations on aircraft reliability, fuel use, availability, and uptime.
• Automotive: Incorporate records of component sturdiness and failure into upcoming vehicle manufacturing plans. Study driver behavior to develop better driver assistance technologies and, eventually, autonomous vehicles.
• Energy: Forecast long-term price and demand ratios. Determine the impact of weather events, equipment failure, regulations, and other variables on service costs.
• Financial services: Develop credit risk models. Forecast financial market trends. Predict the impact of new policies, laws, and regulations on businesses and markets.
• Manufacturing: Predict the location and rate of machine failures. Optimize raw material deliveries based on projected future demands.
• Law enforcement: Use crime trend data to define neighborhoods that may need additional protection at certain times of the year.
• Retail: Follow an online customer in real-time to determine whether providing additional product information or incentives will increase the likelihood of a completed transaction.

Predictive analytics examples

Organizations across all industries leverage predictive analytics to make their services more efficient, optimize maintenance, find potential threats, and even save lives. Here are three examples:

Rolls-Royce optimizes maintenance schedules and reduces carbon footprint

Rolls-Royce, one of the world’s largest manufacturers of aircraft engines, has deployed predictive analytics to help dramatically reduce the amount of carbon its engines product while also optimizing maintenance to help customers keep their planes in the air longer.

DC Water drives down water loss

The District of Columbia Water and Sewer Authority (DC Water) is using predictive analytics to drive down water loss in its system. Its flagship tool, Pipe Sleuth, uses an advanced, deep learning neural network model to do image analysis of small diameter sewer pipes, classify them, and then create a condition assessment report.

PepsiCo tackles supply chain with predictive analytics

PepsiCo is transforming its ecommerce sales and field sales teams with predictive analytics to help it know when a retailer is about to be out of stock. The company has created the Sales Intelligence Platform, which combines retailer data with PepsiCo’s supply chain data to predict out-of-stocks and alert users to reorder.

Predictive analytics tools supply users deep, real-time insights into an almost endless array of business activities. Tools can be used to predict various types of behavior and patterns, such as how to allocate resources at particular times, when to replenish stock or the best moment to launch a marketing campaign, basing predictions on an analysis of data collected over a period of time.

Some of the top predictive analytics software platforms and solutions include:

For more on the tools that drive predictive analysis, see “Top 8 predictive analytics tools.”

Predictive analytics models

Models are the foundation of predictive analytics — the templates that allow users to turn past and current data into actionable insights, creating positive long-term results. Some typical types of predictive models include:

• Customer Lifetime Value Model: Pinpoint customers who are most likely to invest more in products and services.
• Customer Segmentation Model: Group customers based on similar characteristics and purchasing behaviors.
• Predictive Maintenance Model: Forecast the chances of essential equipment breaking down.
• Quality Assurance Model: Spot and prevent defects to avoid disappointments and extra costs when providing products or services to customers.

Predictive modeling techniques

Model users have access to an almost endless range of predictive modeling techniques. Many methods are unique to specific products and services, but a core of generic techniques, such as decision trees, regression — and even neural networks — are now widely supported across a wide range of predictive analytics platforms.

Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that’s used to determine a course of action or to show a statistical probability. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next.

Regression techniques are often used in banking, investing, and other finance-oriented models. Regression helps users forecast asset values and comprehend the relationships between variables, such as commodities and stock prices.

On the cutting edge of predictive analytics techniques are neural networks — algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind functions.

Predictive analytics algorithms

Predictive analytics adopters have easy access to a wide range of statistical, data-mining and machine-learning algorithms designed for use in predictive analysis models. Algorithms are generally designed to solve a specific business problem or series of problems, enhance an existing algorithm, or supply some type of unique capability.

Clustering algorithms, for example, are well suited for customer segmentation, community detection, and other social-related tasks. To Boost customer retention, or to develop a recommendation system, classification algorithms are typically used. A regression algorithm is typically selected to create a credit scoring system or to predict the outcome of many time-driven events.

Predictive analytics in healthcare

Healthcare organizations have become some of the most enthusiastic predictive analytics adopters for a very simple reason: The technology is helping them save money.

Healthcare organizations use predictive analytics in several ways, including intelligently allocating facility resources based on past trends, optimizing staff schedules, identifying patients at risk for a costly near-term readmission and adding intelligence to pharmaceutical and supply acquisition and management.

Healthcare consortium Kaiser Permanente has used predictive analytics to create a hospital workflow tool that it uses to identify non-intensive care unit (ICU) patients that are likely to rapidly deteriorate within the next 12 hours. NorthShore University HealthSystem has embedded a predictive analytics tool in patients’ electronic medical records (EMRs) that helps it identify which chest pain patients should be admitted for observation and which patients can be sent home.

For a deeper look, see “Healthcare analytics: 4 success stories.”

How should an organization begin with predictive analytics?

While getting started in predictive analytics isn’t a snap, it’s a task that virtually any business can handle as long as one remains committed to the approach and is willing to invest the time and funds necessary to get the project moving. Beginning with a limited-scale pilot project in a critical business area is an excellent way to cap start-up costs while minimizing the time before financial rewards begin rolling in. Once a model is put into action, it generally requires little upkeep as it continues to grind out actionable insights for many years.

For a deeper look, see “How to get started with predictive analytics.”

Predictive analytics salaries

Here are some of the most popular job titles related to predictive analytics and the average salary for each position, according to data from PayScale.

• Analytics manager: $72K-$134K
• Director of analytics: $84K-$179K
• Business analyst: $49K-$87K
• Chief data scientist: $133K-$290K
• Data analyst: $46K-$89K
• Data scientist: $70K-$137K

More on predictive analytics:

Google Analytics introduces the following new features that enhance the reporting experience, especially for properties with large and complex data:

Data quality icon at the individual card level. Previously, the Data quality icon appeared at the top of the Reporting snapshot report and the overview reports. However, different messages can apply to different cards. With this update, Google Analytics more precisely shows which messages apply to each card in a report.

Why data quality is important. If a data quality icon appears on a particular card, it may indicate that the data for that card is incomplete, inaccurate, or not up-to-date. This could be due to a variety of factors, such as tracking issues, data processing errors, or user behavior changes. By identifying these issues early, you can take corrective action to Boost their data quality and ensure that they are making accurate decisions based on their data.

In addition, the data quality icon can also help you identify areas where you need to focus your efforts to Boost data quality. For instance, if multiple cards in a report have data quality issues related to a particular data source or tracking tag, you may need to address those issues and Boost data accuracy and completeness for that source or tag.

A new “(other)” row message in the data quality icon. The “(other)” row appears when a report is affected by cardinality limits, which results in the less common data beyond the limits grouping into an “(other)” row. In this new message, you now have several ways to reduce or eliminate these cardinality limits.

All properties now have a one-click option to create the same report in Explore. The “(other)” row never appears in Explore because it uses raw, event-level data.

For 360 users. Google Analytics 360 properties also have access to expanded data sets, allowing you to flag up to 100 reports per property as high priority. Google Analytics permanently removes the “(other)” row from the reports you’ve flagged.

The new message in the data quality icon provides several ways for you to reduce or eliminate the cardinality limits, allowing you to have more complete and accurate data. For example, all properties now have a one-click option to create the same report in Explore, which uses raw, event-level data and does not have the same cardinality limits as the regular reporting. This can help you get a more accurate view of your data and avoid the “(other)” row issue.

For 360 users. For Google Analytics 360 properties, there is also an option to flag up to 100 reports per property as high priority, which allows Google Analytics to permanently remove the “(other)” row from those reports. This can be particularly helpful for advertisers who need to have very accurate and complete data for specific reports.

New sampling controls for Google Analytics 360 properties. The following new sampling controls allow you to adjust the level of precision and speed in your explorations:

• More detailed results: Uses the maximum demo size possible to provide results that are the most precise representation of the full data set
• Faster results: Uses a smaller sampling size to supply faster results (option selected by default)

Sampling is a process that Google Analytics uses to estimate data when the amount of data is too large to process. Sampling is useful for making data processing faster and more efficient, but it can also impact the accuracy and precision of the data.

With the new sampling controls, advertisers using Google Analytics 360 properties can adjust the level of precision and speed to best fit their needs. You can choose to use the maximum demo size possible, which provides the most precise representation of the full data set (this option is useful for advertisers who need very accurate and detailed data for their analyses and decision-making).

Alternatively, you can use a smaller demo size to get faster results. This option is selected by default and is helpful for advertisers who need to get results quickly and do not require the highest level of precision.

Learn more. Visit the Google Analytics Help center here.

Why we care. With the new updates, you should be able to quickly identify and address potential data quality issues, allowing you to make more informed decisions and Boost the effectiveness of your campaigns.

Each customer touchpoint needs to be meaningful. A exact Adobe brand survey found that people are looking for brand communication that is accurate, informative, simple and entertaining. Brands need to have visibility of their customer engagement goals. Match those goals with your data and personalization strategy. And further, identify the experimental approach relevant to your brand and customers.

With insurance penetration rates in India on the rise in both urban and rural areas, ICICI Lombard is committed to offering more and enhanced options to consumers seeking protection against the unexpected. To put itself in a position to scale and accomplish its goals for growth, the company has prioritized a technology-forward approach.

“Initially, ICICI Lombard targeted prospects using a “Quote No Sale” campaign to reach people who previously got a quote on the company’s website but did not end up purchasing a policy.

“With Adobe Campaign, our objective is to leverage rich customer data to create dynamic and relevant campaigns for our customers — through email, mobile, offline channels, and more,” Paromita Mandal, Associate Vice President - Digital Marketing, ICICI Lombard, says.

Venu Juvvala, Country Head of Digital Experience Business Adobe India, says, “In today’s digital world, the insurance sector is transforming from a generalized to a more personalized customer approach. The emphasis on delivering a personalized customer experience at scale has become a competitive differentiator and is crucial for driving successful business outcomes. ICICI Lombard, one of India’s leading insurers, understands the importance of this shift and is elevating its customers’ experience by partnering with Adobe, a leader in digital customer experiences. ICICI Lombard is leveraging cutting-edge technology and expertise to customize its messaging and provide a seamless and impactful digital customer journey.”

Leads that receive IVR calls can express their interest in connecting to a live agent with the push of a button on their phone. Call center agents personally greet callers and quickly assist them with information tailored to their needs, thanks to the personalized information that is automatically passed to the Genesys system.

Seeking to leverage rich customer data

Disposition data, such as whether the prospect answered the phone call, flow back to the system. This enables ICICI Lombard to send follow-up emails or text messages, for example letting a prospect know that the call they received from an unrecognized number is from the company’s customer service center, thereby increasing the prospect’s likelihood of accepting the call on the next attempt. “It’s an automated cycle of real-time two-way data flowing in and out of Adobe Campaign that helps us increase conversion,” says Mandal.

Low-scoring leads may be routed away from higher-cost call center activities and instead toward more cost-effective digital messaging channels. Messages include a link that lands the prospect on the personalized web page where they dropped off, such as a plan page, so they can seamlessly continue their journey.

ICICI Lombard sends Quote No Sale messages to all prospects who don’t initially convert. The revenue from this campaign alone is responsible for 85% of the total CRM revenue from new business by making productive leads readily available. A feedback section in the messages also helps the company understand the challenges leads are facing, as well as any special requirements or expectations they have from the product offering. “The automation has been instrumental in driving profitable growth for us,” Mandal says.

Digital-first renewals Boost retention, cut costs

“Once prospects become customers, we foster loyalty with campaigns that educate customers on helpful, relevant topics, such as how to make a claim,” Nandini Baluja, Senior Digital Marketing Manager, ICICI Lombard, says. “When the company’s call center had to temporarily shut down due to lockdown policies at the start of the COVID-19 pandemic, the digital marketing team sprang into action. At the time, all of the company’s retention campaigns were majorly reliant on call center efforts, including those for customers who purchased policies online.”

“We had to come up with a digital solution to nudge customers for renewal and avert business loss. Within a week’s time, we set up retention campaigns for digital customers,” Mandal says. “The results were highly promising; we were able to achieve renewal rates comparable to pre-lockdown level. This opened a lot of new avenues for us.”

Based on the success of the digital business unit, ICICI Lombard expanded its digital-first renewal strategy to its offline business units. After rolling out a successful pilot campaign for one of the offline business verticals, the company entrusted the digital business unit with managing retention campaigns for motor and health insurance for the entire organization. ICICI Lombard has witnessed an approximate 20% increase in online retention, with 55% of renewals of offline business verticals influenced by the digital renewal strategy.

The team continues to Boost its strategy with deeper personalization, enriched data via system integration, advanced channels and features, and efforts to upsell customers with offerings such as add-on coverage.

Striving to make an even more significant impact on business results, the digital marketing team is working on getting more data into the system. With it, Mandal aims to implement even more detailed campaign personalization, such as tailored IVR message wording or customized images used in a mailer. She says, “With richer data, we can create more intelligent and effective campaigns.”

Brand Connect Initiative

Research from Adobe Analytics has found that the use of Buy Now Pay Later (BNPL) by UK consumers has risen 10.7% compared to January 2022.