Optimizing your promotional campaign with uplift modelling

Transcription

1 ALLMARK FACT BASED MARKETING Amsterdam, 2017 Optimizing your promotional campaign with uplift modelling

2 White paper: Optimizing your promotional campaign with uplift modelling 2017 Optimizing your promotional campaign with uplift modelling 2 Introduction A retailer can generate more sales in many ways. With online shopping as one of the main competitors for physical retail shops, persuading customers to make an additional visit to your shop is one of the key goals of a promotional campaign. Using customer information obtained via a loyalty program together with historical shopping records, a select group of clients can be targeted to yield maximum success. Finding the right group of customers to target is the biggest challenge in setting up a campaign. For example: targeting customers who would visit your shop anyways is waste of campaign money. Similarly, targeting a group who would visit, but after receiving promotional material decide not to visit is the worst outcome possible! In this white-paper we show how to select the right group of customers such that the incremental sales or lift is maximized. First, we briefly discuss some of the current methods used and highlight their strengths and weaknesses. Next, we discuss the core idea of uplift modelling and finally we showcase some of the possibilities. Why uplift modeling? Perhaps the simplest model you can use is a look-alike model. This model tries to find customers similar to those who have purchased some item and persuade them to make the same purchase via a promotional offering. A look-alike model therefore finds people with the highest probability of making a certain purchase. Measuring the success of a campaign based on a look-alike model can be tricky. How do you separate customers who would have made a purchase anyways, from the customers who were persuaded by the campaign? A look-alike model is therefore only practical to use when respondents can only act if they receive a unique marketing contact, via single channel. An example of such a contact would be an campaign offering discounts via a unique web-page and promotional code. A more sophisticated model is what is known as a response model. This tries to model the probability of action after receiving a promotional offering or treatment. Using the control and treatment group, the incremental impact of the treatment can be obtained by measuring the difference in response for the control and treatment group. On the surface this sounds like the ideal model and via a control and treatment groups the effect of the campaign is measured carefully to avoid being too optimistic. However, the model tries to predict the probability of action after a treatment, while the outcome of the campaign is measured as the difference between the control and treatment group. This is silly, because the model is likely to select customers who would make a purchase anyway. You have wasted valuable campaign efforts to give determined customers a lower price!

3 White paper series DataQuest In the beginning of 2000 several new approaches appeared on what is now called uplift or true-lift modelling. In this approach, people are identified who will act only after receiving a treatment. Therefore, it directly tries to model the incremental gain. This is especially useful when respondents have many opportunities to act. Four quadrants Let s take a closer look at the details of uplift modelling. The previous section already showed the need for a treatment and control group to accurately determine the campaign success. If we focus on the binary case where the action is an additional shop visit or not, we end up with four possible options for each customer: TR for response from customers from the treatment group, TN for no response from customers from the treatment group, and similarly CR, CN for the control group. These options are summarized in Fig. 1. Figure 1. The target customers can be divided in four groups depending on their treatment and response. 3 The four quadrants of Fig. 1 help to visualize what a good model should do: it should maximize the response from the treatment group, while minimizing the response from the control group. In this way, the maximum incremental gain or lift is obtained. Finding the optimal group to target We have already seen that a successful campaign requires the use of a treatment and control group, together with uplift modelling to select the optimal subset of customers to target. So far, we have glossed over the precise composition of the treatment and control group. Ideally you would want to clone each customer, treat one and use the other as control. This is unfortunately not possible. Depending on the size of your customer database and the number of features, you can find pairs of customers with identical properties, but it is more practical to create segments. One segment of customers can contain for example males between 25 and 30 years old who in the last month purchased a pair of jeans in one of the three flagship stores.

4 White paper: Optimizing your promotional campaign with uplift modelling 2017 With the segments selected, the next step is to rank the customers from high to low in terms of the likelihood that they can be persuaded with a promotional offering. The effectiveness of the campaign can be represented graphically by looking at the overall campaign profit, in which the cost of the campaign is included, as function of this ranked targeted population. An example is shown in Fig. 2. Figure 2. A gain curve can be used to visualize what fraction should be targeted for maximum profit. 4 Random targeting achieves a maximum profit when the entire population is addressed. In contrast, uplift modelling shows that by correctly targeting 60% of the population, a much higher profit can be realized. The difference is easily explained: uplift modelling only focuses on the persuadable group of people, while random targeting also targets people who would come anyway, people who would never come and people who do not come because they are targeted. Prospects Uplift modelling is a powerful tool to maximize the effectiveness of any promotional campaign. To be successful, the model does require you to know your customer. Only with enough customer details can a good predictive model be made. Furthermore, the data should be clean. For example, a sales employee pointing out a promotional offering at the checkout or using somebody else s loyalty card (bonuskaart) at the checkout pollutes the data. Applying an uplift model successfully therefore might require some changes to the shopping experience. These investments are, however, worthwhile. Knowing your customer well is of big value, not only for a successful campaign. Consider the following example: suppose you as shopkeeper want to organize a promotional event. You can pay for this event yourself, but maybe people can pay you for the privilege to hold their event at your shop. If you can, via uplift modelling, guarantee a minimum turn-out of customers, your event is attractive to market new products. You can hit two birds with one stone: you attract more customers to your shop with an event you are paid for!

5 White paper series DataQuest Case example To boost the Christmas sales, a retailer decides to set up a promotional campaign. The goal is to achieve one additional shop visit per customer during the holidays. Luckily, historical data from previous campaigns is available for uplift modelling. When no historical data is available, a campaign can be used to collect this data. The customer database contains information about past shopping expenses, preferences and other personal information. First, we need to decide which group of customers should be targeted. For each additional customer who will make use of the promotional campaign, a net profit of 21, - is expected. Per regular customer, a single visit returns on average a net profit of 25, -. Therefore, targeting a customer who will visit anyways will result in a net loss of 4, - on top of 1.5, - of campaign money that is wasted. Deterring a sure customer from visiting will result in a net loss of 25, - on top of 1.5, - of campaign money. Figure 3. The gain curve for both an uplift and response model. The uplift model outperforms the response model by more than 30%. 5 Taking all of this into account, we can model the campaign profitability via uplift modelling. The result is shown in Fig. 3. For comparison a conventional response model is also shown. Not only outperforms the uplift model the response model by 30%, you only need to target 50% of the customers, as opposed to 70% for the response model. We also see that the response model first targets customers who visit the shop anyway, resulting in a significant loss. Based on these results, the retailer decides to target 40% of the population. To verify the incremental sales, both the treatment and control group contain 8,000 customers. Figure 4. Results of the promotional campaign. The outcome of the campaign is shown in Fig. 4. The promotional campaign resulted in an uplift of 25.7%, with a net profit of 19k. Not only was the campaign successful, it provided valuable data which can be used to refine the next uplift model.

6 In a data driven society, both DataQuest and Allmark understand how data can be used to gain insight and add value to companies in finance, retail and industry. We can help you with the exploratory phase of determining where value can be added, but we are equally at home with a clearly formulated problem. You are more than welcome for a cup of coffee at either one of our offices to see how DataQuest & Allmark can help your organization. Herengracht 495, Amsterdam info@dataquest.com ALLMARK FACT BASED MARKETING ir Mark Popelier, Harmoniehof 37, Amsterdam info@allmark.nu This report is prepared by DataQuest & Allmark for general guidance on matters of interest only, and is not intended to provide specific advice on any matter, nor is it intended to be comprehensive. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, DataQuest & Allmark do not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it. If specific advice is required, or if you wish to receive further information on any matters referred to in this paper, please speak directly with your contact at DataQuest & Allmark or those listed in this publication. Our general conditions apply to services rendered from us, to our quotations, offers, propositions and calculations DataQuest & Allmark. All rights reserved