Predictive marketing is a marketing technique that involves using data analytics to determine which marketing strategies and actions have the highest probability of succeeding. It has a place in the marketing technology (MarTech) landscape, as companies make use of general business data, marketing and sales activity data, and mathematical algorithms to match patterns and determine the best-fit criteria for their next marketing actions. Companies that utilize this strategy strive to make data-driven decisions to yield better results.
Predictive marketing empowers companies to gain more information about their existing customers in order to identify patterns to predict future outcomes and trends. As MarTech expert Doug Karr explains, it’s not a new practice, but it is a much more manageable one than the former, which required weeks or months of using extract, transformation, and load (ETL) tools to gather data from a range of sources to build a single resource for analyzing and scoring prospects based on their similarity to real customers.
Three main factors have led to the boom in predictive marketing including the massive amounts of data marketers can access from all available sources, access to data in real time, and the introduction of cloud computing that provides Big Data technologies. Now that MarTech tools are more accurate and sophisticated, marketers have access to better results and can measure advertising and audience sources to create campaigns with predictable responses. As MarTech tools become more available and companies begin to see the value of this data-driven approach, there is a shift toward making adopting the technique: Karr reports that 68% of survey respondents claimed they believe it will be a critical piece of the marketing stack, and 82% of companies that claim to be committed to predictive scoring are looking into it as well.
One of the frustrating challenges with this approach is that it may not live up to some executives’ and marketers’ expectations for fully-automated analytics. Jim Sprigg, IHG’s head of CRM, reminds professionals that there needs to be a marriage of art and science to optimize the use of data and analytics: “Humans still have an advantage over computers. We used to call these the big ‘ah-ha’ insights. The sort that come from intuition and highly synthesized recognition.”
One example to prove Sprigg’s point comes from a company that realized offers were accepted differently by customers who used the web and those who utilized customer service: “Humans were synthesizing information along with practical human experience in ways that we would have never known to code into the computer’s consideration set.” It remains a challenge to prepare computers and machines to anticipate all possible scenarios.
Data-driven tools have become more accessible to companies and marketers in recent years, eliminating some of the need for data scientists to interpret data for the practice. And, predictive marketing tools assist companies in using their data specifically to make data-based predictions about how their customers will make purchases, when they will make purchases, and how much they will spend based on their previous behaviors. As the tools continue to evolve, companies are able to utilize automated marketing systems that build models, deploy lead scores, and gain insights in real time.
There are several advantages of having such insight into consumers and their behaviors including improving customer engagement and increasing revenue, gaining more sophisticated segmentation of data, identifying campaigns and actions that are better targeted to customers, better utilizing marketing budgets, and improving lead scoring. Overall, it takes much of the guesswork out of marketing and empowers companies to conduct more accurate forecasting. Forbes contributor Joe McKendrick reminds us that it puts marketers in a better position to see what’s around the bend and perform better in terms of upselling, making next-best offers, determining long-term profitability, and being prepared for shifts in market conditions.
According to McKendrick, Yale School of Management professor of management and marketing and director of the Center for Customer Insights Ravi Dhar cites even more benefits of this approach: “optimizing prices, identifying customer needs more appropriately, machine learning, pricing analysis, unstructured data analysis, text analysis, social media, and predicting what customers will end up buying.” Of course, the common factor among all benefits is having the ability to predict what will encourage a desired customer behavior. McKendrick also points out that it has been delivering desired results for companies: 86% of executives who have been managing predictive marketing efforts for at least two years saw an increased return on investment (ROI) as a result.
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