One of the more hidden benefits of AI marketing – which is only now starting to emerge to the forefront – is the ability to go from reactive to proactive marketing.
What exactly does proactive marketing mean?
“Proactive marketing … allows marketers to be agile, real-time, data-driven, and adaptable to the ever-changing needs … of customers. It encompasses all forms of marketing, and focuses on building strategies with a detailed understanding of … [your] audience … before executing the actual campaign.” – Skyword
A proactive approach allows you to:
- Make multiple projections about the likelihood of engagement, propensity to purchase, and more with specific segments of customers
- Automate execution of the products, content, and offers most likely to resonate with specific groups – and the best time, place, and channel for each communication
Until now, most marketers have been reacting to customers after the fact – post-purchase. This is a major (albeit hidden and unknown) problem because much of the time, communications are too late or irrelevant.
“A proactive approach allows you to make projections about customers including likelihood to buy,” writes @mjbecker CLICK TO TWEET
We live in a real-time world that is quickly becoming even more real-time – it’s becoming anticipatory.
Consumers’ favorite brands like Amazon, Netflix, and Apple are setting the standard by making predictions, recommendations, and projections about what they’re likely to want next. In the mind of the shopper, the inherent expectation is that your brand should as well.
Understanding the Value of AI Marketing
In order to go from reactive to proactive marketing, though, a transformation in your understanding of your customer database has to happen. But it has to happen in such a way that you don’t actually have to do – or analyze – anything. I’m talking self learning, automation, and personalization at scale. A new way to slice and dice data, and then act on it, is exactly what’s needed… a need which AI eloquently fulfills.
“Artificial intelligence would be the ultimate version of Google – the ultimate search engine that could understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing…” — Larry Page, computer scientist, Internet entrepreneur, co-founder, Google
The mechanics of the machine notwithstanding, what’s the real business value of working with AI? As it turns out, there are numerous tangible, applicable benefits, including:
- Understanding the likely acquisition cost and likelihood of converting each individual, as well as anticipating who is likely to churn, defect, and which segments are mathematically worth spending more money on – and with what content/offers
- The ability to understand buying probability/propensity and incentive usage prediction
- The ability to predict revenue and lifetime value (CLV) of segments or customers
Let’s explore the second prong in greater depth since it has the most potential and application.
Predicting the Future: AI and Buying Probability
Those who use AI are prepared for the figurative fight, armed with a barrage of “jabs” of AI-assisted capabilities. But the right hook? Their ability to predict buying behavior.
The heavy lifting behind AI segmentation is its ability to calculate the likelihood of an action happening or not happening. But this information alone as jumbled up data is unhelpful. Marketing organizations will never encounter these distillations and calculations, though. It has to be quantified and labeled. To solve this, you can describe the likelihood with numbers between 0 and 1, and call them “Prediction Scores.”
Narrowly-defined labels or segments create a digestible understanding. The exact thresholds behind “likely to remain inactive” “likely to disengage” or “likely to engage” are dynamically determined by machines from customer to customer.
These descriptive groups actually mean something for marketers. AI algorithms are constantly checking and updating the thresholds.
With this logic, the machine becomes better and better in predicting consumers movement, and marketing teams will see better performing campaigns and learn from them.
Based on your customers’ past behaviors, an underlying layer of AI can predict their future behavior with high certainty.
The “Oz” behind the curtain is a self-learning algorithm that’s training itself with your customer data, finding behavioral patterns, and enabling you to see in the near future, prevent, or encourage customer actions.
RFM Modeling and Buying Probability
The basic model used in production to estimate buying probability is based on classic RFM modeling which is an AI-driven methodology to estimate buying propensity. It takes into account recency, frequency, and monetary value of each individual. This analysis helps to assess, at scale, which customers are of highest value.
In addition, let’s say a marketer wanted to include only customers who bought in the last quarter and also factor in the number of times a contact clicked in an email sent to them. Using an RFM approach, logistic regressions will provide estimates based on data about contacts who clicked at least once or made a purchase in the previous period.
Then, a complete, numeric mapping can be created which illustrates more narrowly-defined dimensions of your database. In this example, 5-5-5s are most likely to buy and buy big, while 1-1-1s are defective and in need of your best win-back work.
In reality, marketing teams will be launching campaigns in phases, and testing different approaches. Ultimately, the intention is for the lower segments to evolving toward the higher ones. Ideally, 1-1-1s continue buying, and move into the 2-1-3, then 3-4-5 — moving toward the 5-5-5s.
The aim of calculating a buying probability for each contact is to estimate the degree of the likelihood of their intention to make a purchase in the future. Once the machine has a projection/probability, it can be used for different marketing purposes (e.g., predicting Customer Lifetime Value or sending incentives in a smart way so that contacts with higher buying probability receive lower incentives and vice-versa).
Unlocking New Dimensions of Your Database
AI enables access to previously unseen dimensions of your database. It helps unveil new aspects about customers you’d never known about before.
For instance, by using engagement score, purchase history and other behavioral patterns, you can predict a contact’s potential CLTV to make projections about their long-term worth to the business.
You can predict their likelihood to purchase to understand where things are heading from a revenue perspective.
You can understand how likely individuals are to visit your website, churn, defect, churn, and even when they’re most likely to do it.
Then, you can decide what to do about it. Automatically tailor products, content, and incentives to each individual, for each use case and across all channels, while taking into account their engagement probability, purchase probability, and next cart value.
In this way, you can understand which customers are worth spending more money on and with what content/offers.
AI is changing the game for e-commerce teams around the globe.
As AI’s primary emerging use case, buying probability research helps estimate a customer’s intention to make a purchase in the future. Once an estimated probability is determined, it can be used for different marketing purposes, like sending incentives in a way that contacts with higher buying propensity/probability receive lower incentives, and vice-versa.
AI-enabled tech is also helping marketers predict customer lifetime value (CLTV), acquisition cost, or churn to understand which customers are worth spending more money on.
Armed with all of this knowledge, previously unforeseen opportunities are revealing themselves like never before for brands bold enough to invest in AI.
Handpicked Related Content: