Cross/Up-Sell & Next Best Offer

 Today global markets are within reach of every organization & there is a lot of pressure in increasing the revenues and market share.  Business leaders demand to know how customers and prospects are most likely to transact with them in the near and long-term.  

The explosive growth of various mediums to reach customers has made marketing an easy but costly affair.

Typical sales & marketing analytics will involve the following:

• Customer Segmentation – Grouping of customers based on demographics data, buying behavior or some other patters which are further used in marketing
• Customer Acquisition – Finding new customers with qualified sales leads
• Cross-Sell / Up-sell – Existing customers present an opportunity to grow revenues through cross-selling and up-sell strategies. Banks can use cross-selling to push credit/debit cards, checking account, investment banking services to savings account holders. Credit Card Company can offer Gold or Platinum cards to Silver cardholders. Use of Social Media Analytics is very useful.
• Channel – Optimize channel performance for sales success by identifying the best leads and channel partners to pursue across channels.
• Churn / Loyalty – Define the right targeting criteria and messaging mix to improve customer retention and loyalty.

Let us look at next best offer in more detail.

Next-best offer is the personalization & optimization of cross/up-sell. It is the use of analytics to identify the products or services your specific customers are most likely to be interested in, for their next purchase.

Here is the typical process for next best offer: 

• Consolidate & create a holistic view of the customers with the vast and growing amounts of transactional, demographic data.
• Apply predictive analytics techniques to customer data & identify trends in purchase behaviors and product affinities.
• Segmentation
• Decision trees
• Neural networks
• Use product affinities/associations to recommend other products or services customers may be interested in.
• It’s very important to close the loop by applying customer responses to future targeting that addresses customer needs.

This coupled with social media analytics of competition offerings or speech analytics of call center data will make cross/up-sell & next best offers more fruitful since they receive critical and indispensable feedback about the issues that matter most to customers, product improvement, service requirements and spending patterns.

Predicting the next product or service, customers are most likely to be interested in, not only improves customer lifetime value but also supports profitable and long-term customer relationships. 

With increasing global competition, optimal customer experience is of prime importance  !!!

Customer churn and Retention

In any business, competitors are always looking to grab your customers, and many customers are on the lookout for a better deal. 

Customer attrition rates range from 7% to 40% annually in various industries. Slowing this customer "churn" rate by as little as 1% can add millions of dollars to any sizable company's bottom line. 

As it is already known customer acquisition is 4 to 5 times more expensive than to retain them, an effective customer retention strategy is crucial to a company's success. 

Marketing departments are traditionally focused on acquiring new customers than retaining existing ones. But after the economic recession, when finding the new customer is especially challenging, customer retention has become a major corporate priority.

While it is the fact that churn will always exist, ensuring that it is managed effectively is a key, to assure profit margins & sustainable business growth.

By predicting which customers are likely to leave, companies can reduce the rate of churn by offering customers new incentives or packages to stay. Apart from that, it gives the best strategy for them in terms of cost and effort by decreasing the total cost of retention and increasing the effectiveness of campaigns.

There are multiple data mining techniques which can be used for churn prediction.

Just to step back, Data mining is a process of extraction and analysis of patterns, relationships and useful information from massive databases. It usually involves four classes of tasks which revolves around classification, clustering, regression and association rules.

Here are the typical steps that are taken to address customer churn & retention:

• Define “Customer churn” as it varies from industry to industry
• Create a single 360-degree customer view
• Collect every Customer touch point data – billing data, transaction records, demographic details, Call center records, Credit history
• Understand the customer behavior
• Map the entire customer journey
• Profiling & segmenting them on various attributes
• Customer value analysis or Life Time Value
• Identify the customers with the highest chances to churn
• Predictive Churn model
• Typical techniques used are Regression, decision trees, survival analysis
• Discover what are major reasons for churn
• Product or Service-related issues
• Demographic constraints
• Better deals from competitors
• Setup targeted retention campaigns for high-value Customers who are likely to leave
• Customized promotions
• Next best offer strategy
• Location-based real-time offers
• Measure the campaign's effectiveness for continuous improvement
• What is the retention rate or how much Churn % has come down

Today social media analytics including Speech analytics is becoming a key aspect to analyze Customer sentiments which helps in finding out the reasons for customer churn. This involves capturing & analyzing unstructured data from customer touch points like customer support call notes, chats, email exchanges & scraping customer comments from Facebook, Twitter, blogs etc.

It is very important to go beyond just predicting customers who are likely to leave & identify the reasons for churn & effectively drive the targeted campaigns to retain those customers.

Use of Analytics in Business verticals.

In the last post, we saw how Analytics has become mainstream & how it is different from the Business Intelligence.

Let us see how businesses are using it for competitive advantage. Today businesses are more worried about survival than profitability. The very purpose of any business to exist is to be profitable & sustain it. This can happen only when there are good loyal & profitable Customers attached to the business.

Hence Customer analytics has become very prime importance in the current time and it is valid across all the business segments.

Customer Analytics:

• Customer Lifetime value – group Customers on high, medium, low value & take actions to increase revenue
• Customer Segmentation – the grouping of Customers based on demographics, or profitability, or lifetime value
• Customer Churn/attrition  - predict which Customers are likely to leave you & take suitable actions
• Customer Retention – identify most profitable Customers & then retain them
• Campaign management – selective campaigns based on segmentation or Customer’s likely behavior
• Cross-sell and Up-sell – increase the revenue proposing other products or high-end products

Apart from these, I am mentioning below some of the areas in business verticals, where Analytics is applied for foresight.

Banking & Financial Services Analytics:

• Anti-money laundering – identifying suspicious transactions to alert investigation officers
• Credit scoring – score the customer based on various parameters to arrive at a certain number and if that is above a threshold then approve the credit
• Credit Risk – predicting the risk involved due to nonpayment by borrowers in case of credit cards, loans etc
• Fraud detection & Prevention – predicting suspicious transactions which are likely to be fraud in all the transactions of the card, wire transfers, online transactions etc
• Price Optimization - Debt collection agency can predict the optimal price for the portfolio & forecast the probable recovery from defaulters

Insurance Analytics:

• Claims Fraud detection  - predicting the claims which are likely to be fraudulent
• Policy Lapse prediction – predict which are the policies that are going to lapse before completing the tenure
• Underwriting rate optimization – predicting the best price for the insurance products based on Customer profile
• Agent performance prediction – how agents are going to add revenues to the organization, improve customer satisfaction & retention
• Agent Lifetime Value – how best an agent is going to serve the organization throughout his/her tenure

Healthcare Analytics:

• Healthcare Claims Fraud detection  - predicting the claims which are likely to be fraud
• Financial recovery – predicting the payments from healthcare insurance payer which are overpayments to service providers
• Health plan analytics – allows organizations to compare & predict different benefits & risk options in terms of coverage & costs
• Condition Management – predict which of the people are likely to develop diseases like blood pressure, cholesterol etc

Retail Analytics:

• Discount or Price optimization – predict the optimal prices of discounts & normal prices of merchandizes  for today’s sensitive shopper
• Cross-Sell & Up-Sell – propose other products depending on various factors such as color, fashion, choice, location, earning patter & Customer buying behavior etc.
• Forecasting – based on demands from Customers predict how much stock is required to avoid stock-outs & excess inventory

Manufacturing Analytics:

• Predicting the parts failure – based on the history data predict  which of the mechanical parts are going to fail & when
• Issue detection – predicting the issues before they occur so preventive maintenance can be done on the parts
• Warranty Analytics – identify issues across the production period to reduce warranty costs
• Forecasting – based on demands from Customers predict how much stock is required to avoid stock-outs & excess inventory
• Inventory Optimization - to reduce inventory carrying costs & increase order fulfillment by predicting optimal inventory to be stored across warehouses

Text Analytics:

• Discover & extract meaningful patterns and relationships from the text collection from social media site such as Facebook, Twitter, Linked-in, Blogs, Call center scripts
• Understand Customer sentiments – positive & negative. Used for Product & Customer service improvements. Also for knowing what competition is good or bad at

Analytics is used in every area of life to get better insights into what is going to happen & what we can do so that the best outcome is expected !!!