Improving Customer Lifetime Value
for the 21st Century Digital Customer


Foreword


Yes, it's almost end of Q1 in 2016, and most of us are living on fumes to execute on our New Year strategies, off-sites and execution plans. 2016 is moving fast with drama all around from politics to financial markets to Oscars to Unicorns so let's take a quick break and reflect on what we have learned in 2015 and share it with the greater community as that's the 21st Century way.

Data is changing the face of our world…

It is estimated that by 2020, we will have nearly 40 trillion gigabytes of data. Data is redefining how you can build smart cities, efficient buildings, predict the occurrence of diseases, optimize utility consumption and many more amazing things.But are companies getting the most from data? Well the truth is certainly not. Gartner claims that 70-80% of all BI Projects fail. They are also predicting that 60% of all Big Data Projects will also fail through 2017. And we at DataRPM couldn’t agree more and really the genesis of how DataRPM got started. Companies must build Data Products that are ingrained in the business workflow to see the real value from data.

Its Time To Be Data Product Driven..

Speaking to Industry Leaders and Influencers, it seems more and more obvious to me that the world is headed towards Data Products.What is a Data Product one may ask? DJ Patil the first-ever national data scientist, White House’s deputy chief technology officer for data policy and chief data scientist explains it in a very simple terms: “a good definition of a data product is a product that facilitates an end goal through the use of data.” With increasing pressures on C-Suite to drive strategic data initiatives that propel them on the path of compounded growth, they have realized that this is no longer a human scale issue. They need to build Data Products like how Amazon did with its Recommendation Engine, LinkedIN did with its “People You May Know”, Uber did with its efficient supply-chain & demand model for cabs using powerful technologies like machine learning and data science.

Digital leadership is a team sport.

As Gartner stated in their CIO Insights, Digital leadership is a team sport, with CEOs expecting their CIOs to be “first among equals.” To succeed, CIOs must rethink and retool their approach to all the layers of their business’s platform, not just the technical one. Once the data is ingrained in this business’s platform, digital transformation will start seeing the value it’s supposed to create.

DataRPM launches the “2016 Digital Consumer” eBook series to discuss about how companies can build great data products and how we are going to be an integral part of the Digital Transformation team.

This eBook is a first in a series featuring Industry Influencers sharing their powerful ideas on how they see the world increasingly shaped by data products built using machine learning and data science.

I would like to thank each one of them to have taken time to help us put together a valuable asset that can be truly enriching for the audience.

Sundeep Sanghavi
Co-founder & CEO
DataRPM

Questions

Top Predictions in 2016.The panel shares predictions on top trends that will be a game changer for enterprises and how enterprises should be prepared
Machine Learning is all the rage right now. The panel shares thoughts on how they are using machine learning for propelling growth and what it takes to build great data products for maximizing customer lifetime value.
Data Science is no longer a choice. The panel shares thoughts on how they are using diverse external sources of data for competitive differentiation to drive compelling user experiences.

"Personalization along with Big Data have stopped being buzzwords and are becoming a major force in understanding the customer and being able to offer up the most relevant assortment/product at the right time and the right price based on their past browsing and shopping behavior, their demographic, their general interests"

Maria Latushkin
CTO
One Kings Lane

2016 Key Trends

2016 Trends

A lthough we can say that these trends are already present this year, they will become more and more prominent in 2016. If I had to pick one, I would say it’s all about the Customer. Customers have more choice than ever in what they buy, where they buy and the channels they choose to buy. For retailers, this translates into the fight to stay relevant to the Customer and there are several ways in which they will be striving to accomplish it:
Personalization
Personalization along with Big Data have stopped being buzzwords and are becoming a major force in understanding the customer and being able to offer up the most relevant assortment/product at the right time and the right price based on their past browsing and shopping behavior, their demographic, their general interests.

Delivery Times
Gone are the days when someone would place and order online and patiently wait for it to arrive. We are being trained more and more to be expecting almost instant gratification with meals and groceries being delivered to our door with lightning speed and goods we ordered online showing up the within a couple of days or even faster in some cases. Whether partnering with local outposts, getting creative with delivery methods or figuring out other solutions, there will be more and more push to improve delivery times throughout 2016.

Customer experience and assisted shopping
Too much choice… With great options comes confusion and reluctance to pull the trigger. A major trend in digital services is to figure out ways to improve customer experience and being able to help a potential customer cross that line. Some brands that have started as digital brands are actually opening physical locations to be able to connect with customers and provide that personal touch, some are offering ‘personal shopper’ assistance. Some are enlisting communities in helping someone make up their mind – carefully curated user generated content, advice on usage of the product etc.

There is no one trend there and every brand will try something new and unique to them, but the overarching theme is the need to make the product stand out through better experience and draw the customer in.

Another trend to watch in the realm of customer experience is making customer’s flow from their phone to their desktop to a physical location seamless. Once you get someone’s interest it’s really important not to lose them along the way as they are less and less inclined to complete the purchase in the same medium and in the same session as they started.


Machine Learning Trends

Do you believe Machine Learning can aid to improve customer lifetime value?

When we think about value creation we usually measure it as number of customers acquired * by their CLV + number of customers retained * increase in lifetime value per retained customer - #customers lost * their ltv – marketing spend. In simple terms, retailers want to acquire more customers, lose less customers and do so in the most profitable way.

Big Data has created a way for us to collect enormous amounts of data. Once the initial excitement about the ability to collect all this data subsided, companies started talking about data deluge and the need to make sense of the data that we collect. According to Forrester most companies think they are taking advantage of about 12% of the data they have to make better marketing decisions. Marketers are now turning to machine learning to find patters in historical data and use that data to predict future actions at a more finite level.

It can help predict customer lifetime value and thus help customers focus on the ‘right’ customer. It can tell us which customers will be very valuable to the company as well as those which will cause us to lose money. We use ML to tailor both our marketing campaigns (offers we send) and product offering in what we show to the customer and the way we present the products. As an example, as the company you want to make sure that any given offer will be able to pull a customer that otherwise wouldn’t have made a given purchase and thus bring additional revenue,as opposed to just subsidizing a purchase for someone who would’ve made the order any way. The important thing is to always have test and control groups so that you can learn from the experiment and further refine it in the future.
Another example is purchase patterns: the same company may have customer segments that are value driven, or prefer frequent smaller purchases or are prone to larger purchases. By tailoring the product selection based and iving different customers specific doorway to your business, you can maximize the revenue from each of them, keep them happy, appear to be relevant to them and thus prevent churn and increase their lifetime value over time.

Data Science Trends

Do you believe Data Science can aid to improve customer lifetime value?

I absolutely agree that data fusion can dramatically increase customer lifetime value.External data is a broad concept as it’s really any information that it collected by an entity that doesn’t have a direct relationship with the consumers. It can be anything from iot to social data to demographic data. All of those are hugely helpful. For example, with a person’s email address through miningsocial listeners you can see what publicly stated interests they have on their Facebook profile, which Twitter hashtags they interact with, what organization they work for etc. You can do sentiment analysis based on what they say in their twitter feed to improve your product selection and/or to target customer service actions and therefore improve their impression of your brand and by extension their CLV. Augmenting your own data with other sources allows you to enrich the data in ways that create a more complete and effective profile, whether it’s demographic, behavior etc. The resulting data set should give you a good understanding of their age, location, life-stage, income, lifestyle and their behavior. The ways in which this data can be used would vary from company to company. For example, a general-purpose retailer selling a variety of goods may detect that someone got married/purchased a home and maybe needs household gifts, became a parent and will be interested in certain type of baby products, and then child products as their kids grow up etc. Someone in luxury goods would be interested in a person’s household income and affinities to other luxury brands.
The data created by ioT devices can give indication on how goods and services are being consumed.