How to use it to drive success

How to use it to drive success

In the face of shrinking budgets, marketing departments are under more pressure than ever to maximize results and optimize every dollar spent. According to Gartner, marketing budgets have fallen to just 6.4% of the company’s revenue, down from 11% in 2021, the lowest level since the research firm began tracking the metric in 2014.

Predictive analytics, a type of advanced analytics that uses statistical techniques, machine learning, and other tools, can help companies get the most out of their marketing dollars.

“This is where companies use different cues from customers and the markets they operate in to understand the meaning of messages, communications, offers, and products,” explained Jiraj Shah, associate professor at the University of Washington School of Information.

What is predictive analytics in marketing?

Business analytics, which includes the field of business intelligence, uses a variety of techniques to identify meaningful patterns in current and past data that in turn can help companies understand what happened, why something happened, what might happen and what needs to be done next to improve it. Results.

Predictive analytics, as its name indicates, is a branch of advanced analytics that predicts what might happen based on patterns in the data collected. Integrating AI technologies such as machine learning into predictive analytics tools now allows companies to analyze massive amounts of data very quickly, enhancing the accuracy and usefulness of predictive models. In fact, many current users of predictive analytics tools simply refer to them as machine learning or data science.

BK's photo.  kenanBk Kanan

Many industries as well as multiple functional areas within any given organization use predictive analytics. Marketing was an early adopter of the technology and continues to be one of the most important users of predictive analytics to help identify which customers to target at what time through channels with what type of message to achieve the greatest impact.

“Predictive analytics is widely used in marketing, to segment, target, acquire and retain customers, to determine which types of ads to display and which will be most effective to display to customers,” said PK Kannan, the university’s dean of marketing sciences. From the Maryland School of Robert H. Smith Business.

He noted that while marketing has long benefited from predictive analytics, the extent of its use varies by company.

Larger, digitally mature companies that have the resources and data volume required for effective use of predictive analytics are in a better position to take advantage of it. Meanwhile, smaller companies with tight budgets to hire the experienced marketers, data scientists, consultants, and technologists required for advanced analytics often have smaller marketing analytics programs.

Organizations entering new markets or launching new products and services lack historical data (known as a cold start problemExperts said) face more challenges to deploy predictive analytics.

Graph showing marketing analytics growth stats

Why is predictive analytics in marketing important?

Analyzing data in order to predict future behavior and events is a classic example of data-driven decision making in business. Using predictive analytics removes doubt, guesswork, and intuition—and the corresponding inaccuracies that come with those—by identifying the most likely outcomes for executives.

As a result, business leaders can have greater confidence that the decisions they make will lead to the end results they seek.

For marketing, that means organizations are in a better position to spend their budgets more effectively, experts said, whether they’re looking to convert leads into new customers, retain existing customers, target consumers based on their customers’ lifetime value or create personalized customer experiences.

Janet Pallis picturesJanet Bales

“More and more marketing leaders are building predictive analytics into their data strategies because it helps them increase efficiency and success,” said Janet Pallis, lead marketing practices at professional services firm EY.

Palese said that predictive analytics allows marketers to “hear more interesting and complex signals from customer insights to inform an accurate marketing strategy.” “It’s about precisely stimulating demand.”

How does predictive marketing work?

Predictive analytics tools, whether in marketing or other functional areas, work essentially the same way: they take in data – the more the better – and then analyze that data using statistical modeling and machine learning algorithms to detect patterns in that data.

Using insights about past behaviors, the tools further analyze the data using predictive modeling techniques to predict possible outcomes in the future.

For example, an analytics system may use the decision tree algorithm, one of the most popular predictive modeling techniques, to determine the courses of actions and the statistical probabilities associated with each action; Branches of decision trees show the possible outcomes of different decisions and how one action will lead to the next, given a whole range of variables.

Owning your data is important. “You can buy third party data mostly for identification purposes, or who you target,” Cannon said. “But if you want to do predictive modeling, you take all the variables that you have about a customer and relate them to their actions. So you want data on your own variables. This allows you to do predictive analysis that lets you see what actions your customers will take with you.”

Chirag Shah photoChirag Shah

Marketing and analytics experts said marketers can choose from a number of ready-made predictive analytics tools with machine learning and artificial intelligence built in.

However, Shah explained that more advanced marketing operations often build their own custom algorithms and tools, seeing this as a way to differentiate their efforts and maximize the success of their organizations.

“It’s also almost a proprietary thing,” he said. “For many companies, the way they derive their insights is the ‘secret sauce’.”

But he cautioned that the in-house proprietary approach to predictive analytics can be costly, and not every organization will be able to afford it.

In-house expertise development options include recruitment agencies that specialize in marketing and analytics services; Hiring consultants to help build in-house predictive analytics; Or outsource your analytics to a service provider.

7 ways to use marketing analytics

Examples of Predictive Analytics in Marketing

Marketing uses predictive analytics to acquire new customers, retain existing customers, and increase sales.

Key predictive analytics use cases that drive marketing success include:

  1. similar modeling. The main task of marketing departments is to acquire new customers, especially customers who will be long-term customers, and they do it efficiently. The goal, Kanaan said, is to get a good return on investment for their marketing efforts. In analogous modeling, marketers use algorithms to predict which individuals from the group are likely to be customers, more specifically, long-term customers. Analytics engines scan data to discover and aggregate the individuals who are most like them and will act like the organization’s existing loyal customers.
  2. Next best action. Based on a large set of data about customers and past behaviors, the algorithms in this approach predict how the customer will react to various marketing actions that could be taken so that the next best action can be taken. This allows marketers to determine which subsequent marketing efforts are likely to produce the best results, and to reduce dollars that would be wasted running ineffective or less effective campaigns. This capability also helps break down marketing dollar silos, Bales noted, which has historically allotted spending into different, isolated categories such as retail offerings and promotions without much coordination between groups.
  3. Lead qualified. Marketers can use the data to predict which consumers are browsing their products and who are most likely to return to finish purchases. “You use analytics to predict the best leads for your company,” Cannon said. This knowledge allows them to target marketing messages to these individuals rather than launching broad campaigns targeting all browsers – an expensive endeavor with lower returns.
  4. High modeling. Similar to the next best procedure, algorithms in Elevated Modeling process data about existing customers as well as consumers’ past responses to marketing efforts to predict how consumers today will respond to various marketing offers. The goal is to answer questions such as, “Should I offer this particular promotion to Customer A or Customer B? Which customers should I target with coupons and which should I not waste my money on?” Bales added that this type of modeling is also effective in determining the best actions to increase sales or complementary selling to customers—essentially getting them to buy additional items beyond the originally targeted purchase.
  5. Proactive management of churning. Shah said that anticipating which customers are likely to leave before they actually do leave allows marketers to step in and try to retain those customers. He noted that this proactive management of pigments is particularly important for broadcast services, whose business model is based on constantly engaging customers. Predictive analytics combined with descriptive analytics allow marketers to determine the most effective course of action to retain customers at risk of leaving.
  6. Demand forecast. Demand forecasting allows marketers to accurately predict how much demand their campaigns will generate so that they can ensure that they get enough of their products or services in the right locations to meet the expected demand.
  7. Data-driven designs. Predictive analytics help marketers determine how best to target customers. It can also help tailor their creative content to the different demographics and locations they serve. “You can use analytics to be completely targeted to the creator, to see what is the best design [content] To force the consumer to take action,” Palis explained. Predictive analytics allows marketers to test different designs—different colors, backgrounds, logos, fonts, etc.—to determine which combination works most effectively with the audience and in which media. “You can test versions to reach the optimum version.

How to use predictive analytics in marketing successfully

Marketers have a suite of readily available ready-made technology tools that enable them to use predictive analytics to shape their overall marketing campaigns and marketing strategies. In fact, Palese noted, many marketers don’t realize the extent to which this ability is embedded in the marketing techniques they routinely use. But she and other experts said that deploying the technology itself would not guarantee success. Some tips include:

  • Focus on having as much data as possible and as much valid data as possible.
  • Choose the right algorithms and modeling techniques for the job.
  • It has processes to reduce biases introduced by faulty algorithms or incomplete or unbalanced training data.

“We don’t have a shortage of tools now. We also don’t have a shortage of data, although a new business or a business entering a new market may not have enough valid data, but even that eventually stops being what it is,” Shah explained.

However, building the wrong model, or misinterpreting it, remains a problem for many organizations.

“It happens by not looking at the fundamental relationship between the variables you’re using,” Shah explained.

For example, model recommendations can be based on correlation rather than causation. “If you have [the wrong] Assumptions are built into the models, the models will be inaccurate.

Another pitfall, he said, “is when not all variables are considered or when there are overlapping variables that are ignored”.

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