To know what the customers will buy next, brings a powerful advantage on the market. Knowing this, you can personalize the customer journey or build the best marketing-mix influencing your customer‘s purchasing behavior positively.
AI-based purchase prediction is one important tool that makes this knowledge possible and available today.
Relevance of Purchase Predictions in Ecommerce
Purchase behavior prediction is a form of predictive analytics often used in combination with permission marketing. Besides the personalization of the customer journey, purchase predictions help your company, among other things, to:
- Improve lead scoring
- Calculate the customer lifetime value
- Increase revenue and customer engagement
To predict buying behavior more accurately, it is important to set apart first buyers from repeat buyers because prediction methods and information needed are different. While for repeat buyer prediction the predictions are based primarily on the purchase history, in the case of first-buy predictions or lead scoring, the necessary data comes especially from the customer acquisition information, e.g., acquisition channel, and only limited initial behavioral data, e.g., time spent on website or sign up for a newsletter.
Whereas many people would argue that lead scoring and purchase predictions are the same, there are several small differences. Unlike lead scoring, purchase predictions can also detect when previously loyal customers churn from your business. Additionally, a purchase prediction can also be used to exactly foresee when a customer is going to make the next purchase. As we describe below, it is also very interesting to combine purchase predictions with product recommendations. Here, it again pays off that we already have a purchase history of customers so that we can recommend products tailored exactly to their preferences.
What is Predictive Lead Scoring and how it works
Lead Scoring is a procedure used to rank probabilities against a rate that shows the anticipated value of every lead for the organization. The produced score determines which leads should have a higher priority for the marketing and sales department.
AI-based predictive lead scoring methods apply machine learning to develop an accurate predictive model. Machine learning algorithms use collected historical customer data – such as demographic information, company information, online behavior, email engagement, social engagement, and similar data points.
You can find more about AI in marketing and sales in our posts Use Churn Prediction to Lower Customer Acquisition Costs and Smart Selling with Predictive Lead Scoring.
The Importance of Product Recommendations
Not less essential for a good marketing strategy is, besides purchase predictions and lead scoring, the question which product an individual customer will buy next.
Here, product preference prediction is a key term. Combining purchase and product predictions are highly beneficial for your company. It is not only possible to forecast which customers are likely to buy when, but at the same time you can also predict which product they are going to purchase next.
Consequently, through the automation of your CRM system, your sales representatives save time which they can invest for other touchpoints of the customer journey. Furthermore, another significant benefit of AI-based product predictions is their accuracy. Several tests showed that the AI-based, individual product predictions are much more precise than simple popularity-based recommendations.
Systematic predictions for future customer behavior give companies an important advantage against their competitors. It provides the business with the unique possibility to reach its clients individually, to increase its revenue, and to establish its position on the market persistently.
Our team is happy to help you choose and establish an AI-based strategy for your optimized predictive lead scoring, purchase prediction, and product recommendations. For further information on predictive scoring and related topics, please send an email to email@example.com.