Analytics has impacted the entire value chain across different industries, be it retail, manufacturing, e-commerce or any other industry. There are primarily 5 different functions in most of the industries which are involved in the production and selling of products. These five functions are Marketing & Sales, Demand Planning, Procurement, Inventory, and Logistics & Transportation. Vendor Management may not be a function in the true sense, but it sits over procurement, inventory, and logistics & transportation. Across these five functions, marketing & sales is one of the biggest beneficiaries of data science. Sales & Marketing Analytics projects have been on rise especially in retail industry. Sales & marketing function is leveraging analytics and data science to bring efficiencies wherever possible.

With the rising competition and increasing customer expectations, it has become difficult and costlier to acquire new customers and retain existing customers. That’s where sales & marketing analytics has provided a ray of hope to marketing & sales teams in organizations. In this post, we will talk about some of the high impact sales & marketing analytics projects which can be quite useful for marketing & sales functions of organizations in retail industry. Other industries can also leverage these use cases with little modification.

1. Recommendation System:

This use case refers to developing a recommendation system to recommend products (or movies or songs) to customers. This is similar to the recommendation system we see on Quora, LinkedIn, Netflix, Facebook, Amazon, Flipkart or any other e-commerce website. There are two types of recommendation systems: content-based and user-based recommendation systems. This use case is very prominent for companies operating in the e-commerce or online space. This helps companies in increasing share of wallet of the customer by engaging them with recommended products.

2. Lead Prioritization: 

Lead prioritization is useful for companies during the sale of high-value items such as cars, flats, etc. You can prioritize customers based on their likelihood (propensity) to buy the product. The more technical term for this is customer propensity modelling. Companies in real estate use this technique to prioritize customers based on their likelihood to purchase a property. Also, automotive companies have developed customer propensity models for their dealerships wherein a sales agent inputs certain data points and predict the likelihood of converting a lead into a customer.

3. Market Basket Analysis: 

Market basket analysis is used for two major purposes. Firstly, it is used to improve share of wallet from existing customers by cross-selling and up-selling. Secondly, it is used to define the placement of products in a brick-and-mortar store. Though this has its own importance for brick-and-mortar stores, companies in the e-commerce space are using this technique for bundling of products. Products which are frequently bought together are bundled together and sold at a certain discount or with some offer to increase average transaction value.

4. Next Best Offer: 

This is similar to the recommendation system but more from the perspective of rolling-out promotions and offers to customers. This technique is used to improve promotional efficiency and increase conversion rates from promotions. Based on a customer’s transaction history and response to past promotions, the company sends targeted promotions which a customer is likely to respond, eventually resulting in a monetary transaction. This is used by both e-commerce companies as well as brick-and-mortar stores. It is mainly based on rolling-out offers on digital media such as emails, Facebook, Instagram, etc.

5. Channel Attribution Modelling: 

This is particularly useful for an e-commerce company to identify which online channels are driving traffic to the site. Since promotions are carried out on multiple digital channels, it becomes imperative for companies to identify which all channels are driving traffic. Based on this knowledge, companies can redefine their advertising strategy and look at reallocating advertising budget to the channels where most of the [potential] customers are present.

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6. RFM Modelling: 

RFM is essentially a segmentation technique based on recency, frequency, monetary. Recency refers to how recently the customer has purchased a product; frequency refers to the frequency of purchases by the customer; while monetary refers to the monetary value of purchases by the customer.
The idea here is that customers in a segment tend to have similar buying patterns and attributes. So, companies can target customers in a segment in the same fashion. For instance, one segment can be defined for customers who buy products worth INR 10,000-12,000 every month. The second segment can be a set of customers who used to buy items worth INR 10,000 every month but haven’t bought anything in the last 3-4 months. Hence, one type of offer can be rolled out to one segment of customers; while a different type of offer can be sent to the customers in second segment.

7. Sentiment Analysis:

This use case is relevant for any and every company which has a presence on any kind of digital media. Social media is no more a platform merely to communicate with your friends, it has evolved into a stronger platform where customers give their feedback and talk openly about their experiences with a brand. Considering the sheer number of people on the internet (translating to the number of customers), it becomes inevitable to track the sentiments of customers and convert them to promoters.

8. Marketing Mix Modeling:

Marketing mix modelling is used to optimize the marketing spend in order to have a maximum positive impact on sales. It essentially includes mapping the impact or effectiveness of various marketing initiatives to the sales of the company. This can include any kind of channel, online and offline, where there was a marketing spend.

9. Customer Lifetime Value (CLV) Analysis:

CLV is a monetary value that a company expects to earn from a customer during the entire customer journey. This analysis has become important in today’s world because companies’ marketing and advertising budgets are shrinking. In such a scenario, the company needs to be prudent in focusing energy only on those customers who are likely to return high value back to the company. For instance, the cost of acquiring a customer is INR 1,000. There are two potential leads: from the first lead, you expect to earn INR 1,500 in the next 3 years; while, from the second lead you expect to earn INR 10,000. Whom would you focus your energy on?

10. Loss of Sales Analysis:

Though this is a very important analysis, very few companies work on lost sales because of limited data availability. Loss of sales analysis refers to analyzing lost sales due to varied reasons. For instance, stock-outs or unavailability of required size in case of apparel could be one reason; while high waiting time in the billing queue could be another reason. High waiting time in the billing queue may result in poor customer experience; thereby, leading to customers not buying the product at all. There could be multiple other reasons for lost sales. This analysis can help companies identify the improvement areas for companies to reduce lost sales. Furthermore, insights from this analysis can have a positive impact on the company’s top line and improve customer experience.

Retail companies carry out multiple other sales & marketing analytics projects for acquiring and retaining customers. Have you done any of these projects? Or, maybe a different one?

We would love to hear your experiences of doing customer analytics projects. Please share your experience us at info@aimonks.com.

Don’t forget to read out the second part: Supply Chain Analytics – Top 10 Analytics Projects