How to Maximize Revenues on Your Customer Loyalty Program using Predictive Analytics?

Editor's Notes

1. S : Hello everyone, Welcome to our Webinar “Maximize Revenues on your Customer Loyalty Program using Predictive Analytics”
2. S : As you might very well know, we are here to talk about how Customer Loyalty Programs can be directed more effectively. So we will commence with a brief background and motivation for Customer Loyalty. This will give us the necessary intuition to define the business problem for Predictive Analytics. We will then digress into a short refresher to Predictive Analytics. Once that is done, we will define the data sources and variables for the problem. Then Kushan would explain the core algorithm and walk you through the Model Implementation steps in the R programming environment.
3. S : So Kushan, thank you for being a part of the webinar! Can you give us a brief idea about Customer Loyalty and why are we focusing on this problem today? K : Sure, its my pleasure to be here. The problem of Customer Retention is not a new problem as such, but it has gained importance lately due to rising costs of customer acquisition. Although, new acquisition channels have come into picture, the cost of acquisition per customer is still very high. Retailers looking to improve their business’s bottom line are better off focusing on repeat customers. This is because they tend to create higher value both in AOV as well as total Revenue. Repeat customers also engage in word of mouth and this in turn has an impact on customer acquisition. The cost of campaign directed at repeat customers tends to be lower than hiring a SEO/PPC Expert so repeat customers have lower service costs. S: Interesting point you mentioned there, Kushan. Repeat customers create higher economic value. Do you have any data to back up your claims?
4. K: Yes, indeed. That point led me to perform some exploratory analysis on actual data. So I pulled up a bunch of Google Analytics eCommerce Data and plotted the proportion of transactions from One Time vs. Repeat Consumers. For a sample size of 5000 customers, the graph clearly shows 85% of the total transactions occurring from the One Time Customer cohort. This is in contrast with just 15% attributed to Repeat Customers. S: Alright, but how does this affect the bottom line of an eCommerce Store.
5. K : The next metric I compared between the two groups was the Revenue contribution. So while the One Time buyer cohort contributed to 60.5% of the total Revenue of the store, the repeat buyers drove 39.5% of the total revenue. Imagine, 750 customers contributing to 40% of the total Revenue. They might either be buying more frequently or buying more high value items per order. S : So what is the take home lesson from this data? K : The key takeaway, in my opinion, would be to focus on trying to shift more customers into becoming Repeat buyers. This can be done either via Customer Loyalty Programs or excellent customer service or some other unique differentiator.
6. K : Even if we are able to convert 5% of these customers to repeat buyers then that is where a tremendous improvement in bottom-line is possible. Does that answer your previous question, Shachi? S : Absolutely, it does! Now that we have understood the motivation for Customer Loyalty, what do you think is a good approach towards ensuring that this happens?
7. K : Sure, one of the popular approach followed is to provide an incentive to customers to carry out a repeat purchase. Discount Coupons really do the trick when sent at the appropriate time. The downside of sending discount coupons to all the One Time Customer Cohort is that since they are not specifically targeted they might have a low conversion rate. The other thing which I notice is if a large number of customers purchase under the influence of discounts, this erodes the margin and there is some revenue loss involved in the campaign.
8. K : Let us discuss this in a bit more depth. Assume that the size of your email prospect list is a hundred thousand and you send out discount coupons to all of them via email. Assuming a CTR of 5% this would lead to 5000 visits on your property. If your conversion rate is 2.5% then that would lead to 125 unique transactions. With an AOV of $250 and a 20% discount on each transaction, the value of discount per customer would be $50.
9. K : Now out of the 125 customers, there would be two distinct classes. Customers who bought only after the discount coupons was provided and the other category would be those who would have made a repeat purchase anyway even without a discount. Then the loss attributed due to these ‘Sure Things’ purchasing under the effect of discount would be $2,500. I understand that figure might not seem very enormous, but there is a definite leakage in revenue and our effort lies in fixing this leakage.
10. K : Dr. Eric Seigel, the author of a very famous book on Predictive Analytics has summed this up very well in one of his blog posts. We talked about two classes of customers in the last slide. So Instead of directing your loyalty program to your entire customer base, you would be better off targeting only those who require some persuasion in order to make a repeat purchase. This, of course, requires us to find the separating line or the delineation between these two customer segments and this is the focus of today’s webinar.
11. S : So finally Kushan, this discussion is leading us to build some concrete basis for Predictive Analytics K : Exactly, we are now at a stage where we can define the business question that we wish to answer using Predictive Analytics. We are trying to predict whether a customer will make a repeat purchase within 60 days of their initial purchase on the website. S : Great, now that we have the question framed well, what are the pre-requisites to answer that? K : Good point, having just the question is just the beginning. We need data at hand and the data we are gonna use is the data that defines key behavior of the customer while making the initial purchase. I will dig deep into this in the next slide.
12. K : The two main data sources are Google Analytics and CRM. Google analytics tells us the medium from which the customer was acquired, the date of the transaction, item quantity, whether shipping cost was incurred on the transaction, product category While the CRM would give us additional information like what was the account creation date, whether the customer redeemed a discount coupon or not. Has the customer subscribed to the newsletter. S : Assuming that we have the data ready, I think we need a way to connect both these data sets right? K : Exactly, that would require a unique Customer ID. In GA this can be got as a Custom Variables after customization and the same Customer ID has to be pushed into the CRM during the data collection stage. S : Okay, got that. Can you shed some light on the tool we are planning to use? K : Sure, we will be using R to build the model. R is a great tool for this purpose and while it might sound intimidating at first it is worth learning R especially for analysis and modelling. We have covered R in 2 of our previous webinars and even on our blog. S : R seems interesting, but one of the questions that I see a lot of people asking is how do you get your Google Analytics data into R?
13. K : Sure, that is a bit of a digression from the topic but lets quickly address the query and then continue. It is possible to get Google Analytics data into R by querying the Google Analytics API. Of course before querying we need to authenticate our account in R. Once that is done successfully, we retrieve the list of profiles associated with our account. We then select the profile to be queried and finally hit the API with a list of dimensions and metrics. That’s was a bit scary but the good news is you don’t have to remember all of it. I am trying to create a mental picture of what the R code actually does. Now lets jump to R and I will show a quick demo. -------------------------------------------------------------------------------------------- S : Now that we have understood the data sources, what’s next on the plate? K : I think before we execute any modelling, I think it would be good to develop some intuition into how a predictive model works.
14. K : Sure, that is a bit of a digression from the topic but lets quickly address the query and then continue. It is possible to get Google Analytics data into R by querying the Google Analytics API. Of course before querying we need to authenticate our account in R. Once that is done successfully, we retrieve the list of profiles associated with our account. We then select the profile to be queried and finally hit the API with a list of dimensions and metrics. That’s was a bit scary but the good news is you don’t have to remember all of it. I am trying to create a mental picture of what the R code actually does. Now lets jump to R and I will show a quick demo. -------------------------------------------------------------------------------------------- S : Now that we have understood the data sources, what’s next on the plate? K : I think before we execute any modelling, I think it would be good to develop some intuition into how a predictive model works.
15. K : Sure, that is a bit of a digression from the topic but lets quickly address the query and then continue. It is possible to get Google Analytics data into R by querying the Google Analytics API. Of course before querying we need to authenticate our account in R. Once that is done successfully, we retrieve the list of profiles associated with our account. We then select the profile to be queried and finally hit the API with a list of dimensions and metrics. That’s was a bit scary but the good news is you don’t have to remember all of it. I am trying to create a mental picture of what the R code actually does. Now lets jump to R and I will show a quick demo. -------------------------------------------------------------------------------------------- S : Now that we have understood the data sources, what’s next on the plate? K : I think before we execute any modelling, I think it would be good to develop some intuition into how a predictive model works.
16. K : Sure, that is a bit of a digression from the topic but lets quickly address the query and then continue. It is possible to get Google Analytics data into R by querying the Google Analytics API. Of course before querying we need to authenticate our account in R. Once that is done successfully, we retrieve the list of profiles associated with our account. We then select the profile to be queried and finally hit the API with a list of dimensions and metrics. That’s was a bit scary but the good news is you don’t have to remember all of it. I am trying to create a mental picture of what the R code actually does. Now lets jump to R and I will show a quick demo. -------------------------------------------------------------------------------------------- S : Now that we have understood the data sources, what’s next on the plate? K : I think before we execute any modelling, I think it would be good to develop some intuition into how a predictive model works.
17. K : Imagine a kinder-garden teacher teaching a student to distinguish between images of a house and a bike Once the images are shown to the child, the child learns that a bike has got round edges and a house has got square edges So when a new image is shown, the student will accurately classify This is the essence behind Supervised Learning. Its called Supervised since the child learnt to distinguish on the basis of his teacher showing him the correct output each time during training. Let us try to map this idea and make it more concrete.
18. S : Thank you, Kushan that was really helpful. I hope the audience enjoyed it as well. We have a couple of questions and the first question is? How trustworthy is the model accuracy metric?
19. S : Thank you everyone for attending this webinar and we hope to see you at our next event. Do make sure you fill up the feedback form after leaving the webinar. We will be communicating with all of you for the webinar recording and the slides. In case you have any questions, please contact us using this email id or via Twitter. Our hashtag is @tatvic.