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5 effective ways to use predictive analytics in marketing

To some marketers, if not most (well most of the ones that I have conversed with). When I mention terminologies such as Predictive Analytics, Artificial Intelligence, and Machine learning they give me this weird look like I am some geek who is trying to look smarter than them. And others find it hard to believe that I actually studied marketing and not computer science.

But as a marketer you don’t have to limit your skillset to only what you studied, because as marketers we are constantly required to wear different hats, have a vast set of skills and bend over backward to deliver amazing work for our clients. But this topic of formal education vs informal education is for another day.

Let’s get back to the geeky stuff – as a marketer, you might see Big Data, AI, Predictive Analytics and Machine Learning as dry and boring stuff but what you don’t know is that every Facebook post like, Youtube video recommendation and subscription, spammy e-mail, banner advertisement, press release, and purchase of any kind—each positive or negative outcome, each successful or failed sales call is encoded as data and warehoused.

Predictive solutions then dig deep into the data to draw insights that determine and inform logical next steps and predict actions that specific consumers might take. This glut grows by an estimated 2.5 quintillion bytes per day (that’s a 1 with 18 zeros after it). 

AI vs ML

Before I delve deeper into explaining the 5 effective ways in which you can use predictive analytics in the marketing realm, let me do you a bit of justice by giving a non-technical definition of the following terms: Predictive Analytics, Artificial Intelligence (AI), and Machine learning (ML).

  • Artificial Intelligence: Is a non-human system that shows human-like intelligence. AI is an umbrella term, which includes machine learning and other techniques. Examples include virtual assistants (Siri and Samsung Bixby), Chatbots, Google Maps, Smart Speakers, etc.
  • Machine learning: Is a method to teach an AI system to perform a task based on the data given to them.
  • Predictive Analytics: Is the analysis of data to see patterns and relationships between variables to identify future outcomes. It enables you, as a marketer to leverage existing customer data to make intelligent assumptions about the activity and behaviour of future customers.

In future articles, I will discuss the use of Artificial Intelligence and Machine learning in marketing and how we can use them as marketing professionals. But first, let me interest you in what predictive analytics can do for you as a marketer now. 

  1. 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 modeling 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.
  2. 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.

  3. 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 bahaviour with the use of predictive analytics. Such will then inform the types of content that most resonates with customers of certain age group, race, gender, demographic or behavioral 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 behavioral habits which then enhances the customer’s experience with your brand.
  4. 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 use 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.
  5. 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.

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.


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