The dream scenario for most businesses is that you can predict consumer buying behavior and identify your most profitable customers. That way when your boss says to you, “Go get us more customers”, you know exactly where to look. But in reality where do you start? Where do you find these elusive high revenue, high margin customers?
The good news is, you have data. You know what a good customer looks like for your organization. But where do you go from here? How do you comb through this data to find that hidden gem of a customer? Why not start with predictive analytics.
Using analytics doesn’t have to be scary, it is a way to predict consumer buying behavior and increase the likeness of your success. Today’s analytics programs don’t take a data scientist to be successful either. Using a likelihood to buy model, you can evaluate both transactional and non-transaction customer data to find your target customer. This includes items such as:
Non Transactional Data:
- How many times a customer clicked on an email
- How the customer interacts with your website
- Social Media Usage
- Past purchase behavior
- Order Total
- Use of Coupons or Discounts
- Order Recency
- Order Frequency
Now what do you do with this data? The predictive models compare the pre-purchase behavior of prospective buyers to the pre-purchase behavior of thousands or millions of previous customers who ended up buying, comparing attributes such as what emails they opened and what products they spent the most time looking at. The prospects that behave most like the previous buyers are tagged as “high-likelihood buyers”. You can then alter the way your organization interacts with these prospects to increase the likelihood of closing a sale. Once you’re armed with this data, you can prioritize your investment in each prospective customer.
Not All Prospects are the Same
Using the likelihood to buy predictions you just completed, you can now tailor your discount offering. Prospects who are already more likely to buy won’t need as aggressive of a discount as customers who are less likely to buy. There’s no need to give up precious margin on a prospect that is already going to purchase the product from you. But a prospect that is less committed to your product over a competitor can be swayed by a good discount. You can run predictive analytics on the frequency of purchase model to see how often a customer typically purchases a product from you. If they are outside their normal range of purchase, you can offer a discount to sway them back to purchasing from you again.
It is not only important to predict likelihood to buy for first-time buyers, but it is equally important to predict likelihood to buy for repeat buyers. The repeat customers is the chicken that keeps on laying eggs for you. Predicting likelihood to buy for repeat buyers is a lot easier than predicting likelihood to buy for first-time buyers because there is a lot more information to review. You can look at their buying habits such as frequency, whether a coupon or sale triggers a purchase, holiday shopping behavior and more. The likelihood to buy model for repeat purchases evaluates earlier transactions as well as other interactions similar to the model for prospects.
If you don’t understand your customers, I’ve seen this lead to blanket discounts, which reduces overall margin. Some customers will need a bigger discount than others to convert, the key is knowing which ones and the right time to offer the discount. Using a monetary value analysis, you can see the average amount a customer with their particular purchase history spends with you, then offer a discount code for a percentage off at a higher buying threshold than they normally spend, pushing their shopping cart up past their normal purchasing behavior.
Lifetime Value of a Customer
Knowing the lifetime value of your customers is also a key to success. The customer lifetime value (CLV) is the present value of the future cash flows attributed to the customer during his/her entire relationship with the company. Using predictive analytics, you can improve the customer lifetime value of your customers by knowing which segments to target. One example would be to run a recency model and look at which customers have purchased products most recently. By looking at customers that have not made a recent purchase, you can offer a discount to customers with no recent purchases to win them back as customers, essentially preventing customer churn.
Your models will get better over time as you collect more data. If you keep fine tuning your models, your predictions actually become reality. You will begin to predict consumer buying behavior with increasing accuracy. Predictive Analytics uses algorithms to identify previously unrecognized patterns and trends hidden within vast amounts of structured and unstructured information. The more data it has, the more accurate the algorithms become. These patterns are used to create predictive models that try to forecast future behavior. The challenge is to extract real meaning and business results from your data, and that takes time and testing.
Ready to Learn More?
Learn more about predictive analytics and how it can help you identify your most likely to purchase prospects and customers. Read our free predictive analytics white paper to learn more.