Ways to Improve Customer Lifetime Value (CLV) Using Analytics

Customer Lifetime Value (CLV) is the estimated net profit a business expects to get from the entire duration of its relationship with a customer. A high CLV is desirable as it implies increased profits and higher levels of customer engagement. Analytics is today used by mature organizations across the different stages of the customer lifecycle in order to increase CLV.

Acquisition:

While customer acquisition is a key performance indicator for many businesses, many companies fail to measure two important parameters – the Cost of Acquisition and projected Lifetime Value. Spending too much money acquiring customers with low lifetime values will corrode a company’s profitability.

This can be avoided by using look-alike modeling. In this technique, we profile existing high value customers across multiple dimensions and identify prospective customers that show similar patterns early in their customer journey. A 2014 study by eXelate showed that 50% of the advertisers who use look-alike modeling experienced a 2-3x higher lift in CLV.

Engagement:

“Attracting a new customer can cost as much as 15 times more than retaining an existing customer,” says Terry Gillen in his book “Winning New Business in Construction.” It is a lot easier and cheaper in engaging existing customers. Apart from this leading to an increased CLV, it also influences brand perception positively. A few common analytics practices used to increase CLV by increasing engagement include:

a) Marketing based on Customer Segmentation:

By using analytics, Marketers can micro-segment their consumer base across multiple dimensions.

• Demographics including age, income, gender. In the case of B2B organizations, factors like size of the business, industry could be used
• Purchasing patterns of customers
• Customer preferences and affinities

Once accurate segments have been created, marketing programs are created for each segment. For example, customers who have high purchase intent in buying back-to-school clothing could be offered a basic discount that could influence their purchase choice.

b) Cross-Sell:

By bundling relevant products together, cross-selling leads to higher engagement and value as it increases the breadth of the relationship. Item based collaborative filtering can be used to provide high impact, relevant cross-sell suggestions at every consumer touchpoint, rather than randomly bundling the items based on generalized usage assumptions.

c) Survey Analytics:

The results of surveys can be effectively used to update the usability of the products and tailor marketing messages to consumer preferences and needs. This leads to increased conversion, thereby leading to greater CLV and engagement.

Churn Prevention

Customer churn refers to the number of acquired customers who have become inactive. Churn impacts profitability significantly as the cost of acquisition has been borne without realizing profitability from the customer engagement.

By using analytics, every customer can be given a Churn Score. By building propensity models, customers with a high churn score – or the greatest probability of lapsing in a fixed period of time – can be identified. These customers could receive special marketing attention – more offers and increased customer support- to ensure that they remain engaged. This is especially impactful in increasing CLV when it is combined with high lifetime value scores.

You may also find it interesting to read our blog on how to master customer retention through data and analytics or how bad customer experience can hurt your brand (the Comcast Story)

Reactivation:

Re-engagement campaigns are an important marketing strategy. However, companies need to use analytics to identify which customers to re-engage with and with what messages/offers. Historically, re-activated customers bring in lesser value in the initial months after re-engagement. However, with the use of analytics, the difference in CLV between engaged and reactivated customers can be bridged significantly.

These are some of the more common ways in which global companies are using analytics to increase CLV. You can also look at how eBay uses data for competitive advantage.  To know more, email: marketing@latentview.com.

http://www.latentview.com/blog/ways-improve-customer-lifetime-value-clv-using-analytics/

How The Bubble Blew: Using Social Media Analysis To Understand Declining Sales

Social media analysis is a powerful tool to understand trends. When trying to understand the reasons behind the declining sales of chewing gum, we turned to social media find the answers. We first studied the top usage occasions, when gum was chewed, in 2010. We then looked at those occasions to understand how they have changed. In each and every one of those occasions, we found alternate substitutes to chewing gum. For example, people chewed gum out of boredom in 2010 (6.7%). Smartphones have completely taken over this usage occasions, pushing chewing gum use to a negligible .3%.

You may also find it interesting to read our blog on how social media is transforming the CPG and Retail industry.

http://www.latentview.com/blog/bubblegum-social-media-analytics/

Mastering Customer Retention Using Data

In the halls of marketing fame, customer acquisition stories get all the attention while those involving retention are rarely discussed with the same fervor and enthusiasm. Yet, most marketers will admit in private that customer retention is the single most important priority and an integral part of their strategy, which significantly impacts their bottom line. According to global consulting firm Bain & Company, it costs six to seven times more for an organization to acquire a new customer than to keep an existing one.

Increasing customer retention and loyalty is particularly relevant for retailers as they attempt to grab market share in a competitive environment. But how exactly can retailers identify and create loyal customers? Mere numbers — visits to a website, for example — do not tell us the full story of brand affinity unless they are also accompanied by other critical indicators regarding attitude or perception, and sentiment or advocacy toward the brand.

In creating a loyal customer, retailers need to first segment their customers based on their specific phase in the retention cycle. During each phase, data analytics can aid and support their outreach and marketing efforts.

When it comes to acquiring a new customer, retailers typically use a variety of channels to draw customers into their stores. These include advertising, promotions, affiliate programs, referral programs, coupons, rewards and other incentives. By effectively leveraging data analytics during this phase, retailers can ensure that these efforts are more than just random attempts at attracting customers.

How exactly can data help? Some of the ways analytics can be leveraged include better attribution insights or clues into what leads prospective customers into their stores. By identifying and gaining insights on better understanding the motivation of potential high value customers, retailers can redesign their promotions and product mix to attract such customers.

There are several ways in which a combination of internal and external data from CRM systems, transactional reports and demographic databases can be pieced together to get a better picture of what drives a customer to consider a purchase. When such information is combined with geographical data gleaned from mobile devices, it gives retailers a location-based map of customer sentiment and inclination. This can help them target customers with timely promotions and updates. For example, a customer could get a notification about a special discount on running apparel in a sporting goods store as soon as they step into a mall where that store is located.

Retailers often believe once a customer enters a store, online or brick and mortar, and make a purchase, they are a potentially loyal customer. They are entered into a database and the retailer begins treating this group (customers who have made a purchase) as a homogenous entity, running campaigns and offers to convert them to “loyal customers.” But they often do not meet with much success and have a very low conversion rate. The reason for this is customers all have different stages of brand involvement. To begin with, it takes a certain number of transactions before a customer and their purchases can be classified as intentional and not merely impulsive. What that number is changes from business to business, but a look at historical data can very quickly help define that threshold.

It is within this customer set, the ones that have reached the purchasing threshold, that you begin looking for “loyal customers.” Analytics can help create models based on historical data as well as external data, like wealth credit scores to predict high net worth customers, or common patterns of behavior, sometimes referred to as DNA markers. Customized campaigns directed at this segment will have greater success and higher conversions.

Ultimately, customer experience and satisfaction holds the key to driving repeat purchases. This is certainly more important in some sectors versus others. In the travel and hospitality sectors for example, positive customer experiences are essential for repeat transactions, while negative experiences could stifle this portion of business completely. Again, retailers can turn to data analytics in order to isolate bottlenecks and constraints that result in poor customer service or — in the case of an online business — cause customers to abandon their purchases.

As is clear from all of this, customer retention is a critical process that requires an understanding of where a customer or prospect fits in the retention cycle. With this understanding in place, data analytics can come in to help marketers cultivate the quality they most value in their customers — loyalty.

Mastering Customer Retention Using Data

Analytics Maturity Survey

There’s no doubt that analytics can provide valuable insight to drive business value. But having the right analytics is just one part of the equation – enterprises must be able to capture and use it effectively.

When it comes to advanced analytics, how mature is your organization?

Take our free online assessment to find out – and see how you stack up against the competition. And be sure to get your free copy of “The Five Stages of Analytics Maturity,” which helps you recognize the five stages and provides recommendations to help you move forward to the next stage.

Analytics Maturity Survey