According, to research the predictive analytics market is growing at a 23.2% from 2019 to 2025 and its market size value in 2020 was $8.2 million. If I were you I would start getting worried about my competitors becoming advanced users of predictive analytics in marketing.
However, the challenge is that most professionals, especially those in the marketing space don’t fully understand what predictive analytics is and how it can be used in the marketing sphere.
But, before delving deeper into the technicalities, let me first explain exactly what this “Predictive analytics” is.
What is predictive analytics?
Well, in the simplest terms, predictive analytics is the act of using current and/or historical data with a combination of statistical techniques to predict the likelihood of a certain event happening in the future.
To clarify what predictive analytics is, let me compare it to other fields of business analytics (hopefully I don’t confuse you even more):
- Descriptive analytics answers definitive questions like “what happened?”
- Predictive analytics answers hypothetical questions like “what is likely to happen?”
- Prescriptive analytics takes predictive analytics one step further by answering questions like “what should we do based on what has already happened and what is likely to happen?”
If you are wondering, how predictive analytics works in marketing and how you can benefit from its use as a marketing professional – please follow me as I answer a question that I get asked a lot “What is predictive analytics in marketing?”
What is predictive analytics in marketing?
The truth of the matter is that marketers have long leveraged data to understand and improve campaign effectiveness and these efforts have advanced over the years.
Marketers used media mix modelling, which allowed them to understand the long-term impact that a campaign had on sales, which helped guide future campaigns optimisation efforts.
And they later advanced to attribution modelling which moved beyond aggregate data, and toward user-level interactions which allowed marketers to understand consumer paths to purchase.
Since data-driven media planning has evolved, now it’s time for marketers to look into new and advanced tools like predictive analytics.
In the marketing context, predictive solutions can help in digging deeper into the data to draw insights that determine and inform logical next steps and predict actions that specific consumers might take.
Common use cases of predictive analytics in marketing include smart lead scoring, predictive customer behaviour, advanced content distribution, predicting customer lifetime value, products recommendation for up-selling and cross-selling, customer audience segmentation, new customer acquisition, content and ad recommendation and personalising the customer experience.
Let me take you through some of these use cases in more detail.
Predictive analytics in marketing use cases
- Smart lead scoring: Because we can’t completely rely on our human intuition to predict which lead is most likely to convert, with the power of predictive analytics lead scoring becomes less of a manual task and more of an actual data-driven view of your target customer. Predictive modelling enables predictive lead scoring which will help inform the next step in marketing to a prospect based on predictions about their future buying behaviour using historic, behavioural data.
- Predicting customer behaviour: With the help of predictive analytics you can understand what your customers want when they want it, and in what manner – which helps you better anticipate behaviour and reaching them across multiple channels with the right message at the right time more effective. Predictive analytics can further help identify unhappy customers you’re in danger of losing – as well as satisfied ones who may be ready to buy.
- Advanced content distribution: Content can only be king if it resonates with its kingdom. As a marketer, you can distribute accurate and relevant content through an analysis of customer behaviour with the use of predictive analytics. Such will then inform the types of content that most resonates with customers of certain age groups, races, gender, demographic or behavioural backgrounds. And then with the use of a content management system, automatically distribute similar content to customers that mirror the same age, gender, demographic or behavioural habits which then enhances the customer’s experience with your brand.
- Predicting customer lifetime value: The same predictive strategies that help to accurately predict customer behaviour, content distribution and lead scoring can be used in predicting customer lifetime value by taking historical data of each customer and using it to forecast their lifespan. By looking at the historical lifetime value of current customers that match the backgrounds of new customers, you can simply make a reasonable estimate of a new customer’s lifetime value. The predictions can help set budgets for customer acquisition, which will give a more accurate and expected ROI.
- Product recommendation for Up-selling and cross-selling readiness: With the data that’s already available about the customer’s potential future behaviour, content consumption, lifetime value and their score as a lead, as a marketer you can recommend relevant products for up-selling and cross-selling to increase revenue and marketing ROI.
- Customer segmentation: If you don’t know whether you should segment your audience based on their behaviour, demographics, firmographics, interests, or any other variable, predictive analytics can help.
- New customer acquisition: According to Pinja Virtanen, you can use your customer data to create identification models. In practice, this comes down to identifying and targeting prospects that resemble your existing customers in some meaningful way.
A common example of this is Facebook’s lookalike audiences. You can use this feature to upload a list of the emails of your best customers, based on which Facebook starts targeting your ads to people similar to these customers.
Final thoughts
Predictive analytics will help forecast marketing performance and strategies based on past campaigns, scoring leads more accurately, uncovering opportunities to improve understanding customers’ behaviours, delivering more meaningful content, predicting the lifespan of customers, recommending products that enhance their experience and open doors for up-selling and cross-selling. With the power that technology presents into the marketing realm, data and predictive analytics are no longer dry and boring stuff but must-have skills if you need to be ahead of the game as a marketing professional.
