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Mayank Reddy

Mayank Reddy is Product specialist- Customer Analytics at Manthan. Mayank works closely with businesses in helping them adopt and leverage analytics to derive maximum value out of their data. Mayank has extensive experience in the Marketing/Customer analytics and has worked closely with a range of CPG, E-Commerce and QSR companies and helped them in their analytics journey
Look-alike modeling for better cross-sell, upsell and growth marketing

Don’t just look out, look in too

Look-alike modeling for better cross-sell, upsell and growth marketing

“Find me star customers” is just about every marketer’s ask. This is where social media platforms such as Facebook and Data Management Platforms (DMPs) boast of “look-alike modeling” and serve customer acquisition scenarios. Look-alike essentially creates a new audience of people who resemble your existing best customers.

Conceptually, this is how it works – you’ve got your best customers, you derive the defining characteristics of these people, and leverage that insight to get more such people.

Want to get technical? This is how Gartner describes the process.

“So once you’ve built up your model audience, the DMP will look to see what you know about these people – what their attributes are. These attributes are then bumped up against the “internet population” (often), which is simply a model of online adults. By comparing these two groups – model audience vs. internet population – you can see which attributes define your audience and which do not by seeing where they over-index (or under index) vs. the broader population. Then you create another audience using the over-indexing attributes, purchase targets from third-party vendors, and serve them ads.”

Marketers agree, and they get decent returns with this approach.

What is the opportunity then?

A look-alike modeling for customer marketing and growing share of wallet

The above approach so far has been leveraged for customer acquisition use cases only, i.e. social, search and display ads.

How about finding relevant customers for a given marketing goal from within our existing customer base?

Let’s take an example. Direct marketing team is looking to find 3000 people who’d engage with the newly launched organic range. Your current customer base has 45% males and 55% females. The look-alike model will find you the relevant group for the organic range campaign, and you discover that it has 80% females and 20% males (i.e. it is over-indexing on females). Hence, you know that females have a higher inclination for the organic category. For simplicity, this example has only attribute – gender, but in reality, there might be many attributes that are used to create the actual list of 3000 (such as age range, income levels, family size etc.)

This is where the rich first party data within their CDP (Customer Data Platform) is leveraged, to come up with powerful and accurate lookalike lists.

We call this inward audience finding. This is hard because there could be 100s of customer attributes and 1000s of base measures, from which the defining attributes need to be gleaned. The lookalike model we have built, is first of its kind in the industry, and provides marketers the opportunity to find the best target list (as many as you want based on your budget) for a given objective.

Acquiring new customers who are like your star customers is important, but I’d argue that nurturing potential stars within your customer base is non-negotiable. So, don’t just look out for stars, look in too.

Increase Same Store Sales

Increasing competition, rising costs, wage inflation etc. have led to disappointing same store sales growth and declining traffic counts for the restaurant industry in the last few years and this trend is going to continue for the next few years. Read report. Its important to understand what the key drivers of business are and focus your energy and resources on things that matter. Growth in sales of a restaurant chain can be achieved by a combination of Market expansion and Same store sales growth. For any restaurant, the most important metric for success is the same store sales growth. What is Same Store Sales growth you ask? Its increasing sales of existing restaurants over time. While expanding the market and increasing customer reach is necessary and strategically important, same store sales growth is a strong indicator of health of the business and is the only sustainable solution in the long term.

The 3 main factors that affect same store sales are:

  • Number of guests visiting the restaurant
  • Number of times each guest is visiting the restaurant
  • Amount each guest is spending per visit

Number of guests visiting the restaurant

Growth can be achieved by adding new guests, however the challenge, specially in matured markets for restaurant chains is that the scope to keep adding new guests goes down with time. This is amplified when there is intense competition, large number of restaurants and a small area under each restaurant. Restaurant Marketing Metrics notes that acquiring a new customer is six to seven times more expensive than keeping an existing customer. Thus, while bringing in new guests is strategically important, it is not easy and the success of a restaurant is really determined by its ability to make the most of its repeat customers.

Amount spent per visit

In F&B unlike other industries there is a limited capacity to consume in a visit/order. After all, the “basket” can only be so big! While this does not mean that there is no upsell/cross-sell potential, there is potential to fill the basket when the guests are not purchasing enough items. For example, adding sides and beverages and replacing low value items with high value items etc. The scope is definitely limited.

Visit frequency of guest

Visit frequency of a guest is the number of times a guest visits a restaurant in the period, as basket value has limitations in terms of the scope for growth visit frequency becomes the most important driver of sales. More about increasing the frequency of guests at your restaurant in my other blog. If you are interested in increasing your same store sales growth, you should check out what Manthan is doing in this area. They have some really cool success stories in this case study and offer further tips for your restaurant in this data sheet. Feel free to reach out to me directly if you have any thoughts or questions about this article. Also, read our blog “Advanced Analytics in The Restaurant Industry” to learn more about Restaurant Analytics and Restaurant Marketing.

Increasing Guest Frequency in Restaurants

For the un-initiated, visit frequency of a guest is the number of times a guest visits a restaurant in the period. It is an important driver of sales, if not the most. So the big question:

How can you drive guest frequency in your restaurant?

To increase visit frequency, it is important to understand how guests perceive you and why they are or aren’t coming in as regularly as you would like. What makes a guest visit and continue to revisit the restaurant is the overall experience he/she gets. Experience can mean different things to different people but is mostly a combination of:
  • Taste/Menu
  • Perceived Value (Price)
  • Service
  • Ambience/Atmosphere
Identifying/Understanding what drives a particular guest to revisit is not an easy task for sure. The guest doesn’t explicitly talk about their experience (at least not all of them), however guests still tell us this through:
  • Their orders
    • What are they ordering?
    • When are they ordering?
    • How are they ordering?
    • How much are they ordering?
  • Their in-store behavior
    • What do they do in the restaurant/on the website/on the app
  • What are they saying in
    • Surveys
    • Feedback
    • Social media
It is very important to leverage data from all these sources to understand the guest and personalize the experience based on what you know about him/her. You can do this through:
  • Guest Segmentation
Segment guests into actionable groups based on purchase behavior, taste etc. and leverage insights about segments to design unique marketing campaigns that influence loyalty and frequency of visits.
  • Digital Engagement
Drive conversion on your web and mobile channels by understanding behavior, preferences and content engagement on those channels.
  • Real Time engagement
Sense and respond to specific guest events at POS, ecommerce and mobile apps with relevant, timely messages.
  • Feedback Insights
Analyze feedback, survey, social data to get insights about guest experience which can then be used to engage with guests appropriately. There is so much you can learn from the actions and achieve the optimal level of personalization for your guests but it’s an uphill battle without the right tools. I recommend you check out what Manthan is doing in this area.  They have some really cool success stories in this case study and offer further tips for your restaurant in this data sheet.