Personalization

Customer development for eCommerce

or when does Conversion Rate Optimization make sense?

Every eCommerce marketer knows about need for better conversions. However, sometimes marketers think that all visitors to the site are equal and should convert to buyers. Many times they look for the shiny object (Conversion Rate Optimization) that will get them there. The shiny objects I refer here are different for different people, but are generally what the marketer has read about recently. This would include running analytics to find what pages are not “converting” or running heat maps on not so well converting pages or running A/B tests to find better converting pages.

Putting customer development ahead of optimization.

While these are important tests to run, they make better sense if an earlier step of customer development has been done. Indeed running analytics to understand customers is more important. If paying for ads, for example, are they targeted to an audience that will be willing to make a purchasing decision? Is the target known and willing to buy?

This is called as customer development. Steve Blank, consider the guru of customer development, says “you need to leave the guesswork behind and get outside the building”. The guesses or assumptions have to be converted to reality by getting in touch with customers and talking to them. The talk could be in terms of surveys, secondary research, etc. The attempt is to find the market to which the assumptions hold true.

Steve defines a four-step framework for Customer Development – Customer Discovery, Customer Validation, Customer Creation and Company Building. Even if you have money, spending money before Validation is a waste, perhaps disastrous. Instead money has to be used to grow the business.

Customer Development Model : The Four Steps to the Epiphany


While applied to software / SaaS businesses, this applies to eCommerce as well. Infact, Steve uses an eCommerce example. The beauty of the model is that it is iterative as Steve says “… finding the right customers and market is unpredictable, and we will screw it up several times before we get it right”. As new product categories are introduced, the same steps can be used to build a market.

Example

Let us take an example – if you are a diamond jewellery store online your market is NOT every woman wanting a diamond – instead it is that subset that is interested in buying the diamond now. If you run a nationwide TV ad, you will get a lot of hits to the site – women are generally interested in jewellery. If this data is used for conversion optimization, incorrect conclusions could be drawn. Instead if this data is used to study the customers who actually purchased, you can get some useful information.
For example, the success page could be a short survey of their purchase experience. Better, if the purchaser’s journey can be accurately mapped – to the products they saw, the products they added / removed from cart and the length of their visit. If this was then time mapped to the ad that was playing at the time, you could map the actual ads that converted better. A geo map could help in the exact location of the purchase.

Using Data for Customer Development

So let us say we get the following data from a nationwide campaign

Metric : Geography

Customer Development Analytics -Geography

Metric: Age Group

Customer Development Analytics - Age

This can be analysed to indicate that Geo2 is possibly an affluent neighbourhood but is converting very well. Geo3 is not an affluent neighbourhood but people like to look at expensive jewellery and not checkout.

We also learn that most women do not like to give their age which indicates not a good metric to use.

Without being in the customer validation mode, we would try to optimize the site based on overall cart abandonment data. Perhaps displeasing the Geo2 users, leading to a decline in sales from a well converting segment.

From Search to Execution

During customer creation we may decide that a good model is to identify the Geo2 customers and to drive demand in this geography for the type of products that they like. Perhaps one might want to even personalize the site so it offers the kind of products that this segment likes.

If you need help with eCommerce data including Customer Development, you can reach us at sales_at_rrap-software.com

Personas in eCommerce

Personas in eCommerce

Traditional marketing has used personas for marketing typically using census data and demographics. However, recent research shows that traditional demographics are fast breaking due to various reasons. For example adage recently published an article titled (“In a Digital World, Are Generations Dead?”) Though very relevant to the US, the idea of demographics not being the only way to target is getting real.
 
As eCommerce store owners, we naturally think demographics – region, gender, age, etc. However, those are not the correct personas and targeting so is making a mistake.
 
Personas however are extremely important in eCommerce. In this article we will review some material on personas as it applies to online marketing and eCommerce.
 
Seth Godin in his famous book “Tribes: We Need You to Lead Us” uses the word tribes to describe “A tribe is a group of people connected to one another, connected to a leader, and connected to an idea. For millions of years, human beings have been part of one tribe or another. A group needs only two things to be a tribe: a shared interest and a way to communicate.”
 
Seth’s description of tribes is perhaps the most transformational in understanding online behaviour. What it means is we need to study all the data we have about our visitors and customers and derive what is a tribe for our complete or a part of our store.
 
In 2013, Mastercard published a very well researched article on personas based on how willing they are ready to share online – and effectively said there were 5 key ones each representing 20% of the market. This is very relevant to eCommerce as if it were possible to classify your visitors into these personas, you could decide how to market to them!.
 
Open Sharers, 21% of the population, “are particularly aware of targeted marketing, with nearly full understanding of the value of their data and how merchants, marketers, and consumers interact online. When they share their personal information, they expect deals, access, and offers in return.”
 
Simply Interactors, 21% of the population, “are aware of targeted marketing, but don’t see their data as that valuable, and so do not express significant concern about. . When looking to shop online, seven in 10 Simply Interactors will look for reviews about unknown companies before making a purchase.”
 
Solely shoppers, 21% of the population, “generally do not see their personal information as valuable to merchants and advertisers, though they appreciate getting special offers and discounts for checking into a store. They have very little awareness of targeted marketing, as only 37 percent of this segment are aware that social media sites use their personal data to inform targeted ads.”
 
Passive users, 20% of the population, “are more willing than other personas to trade their data for something in return. Passive Users are not heavy online shoppers, but they are more likely than other personas to shop via a mobile device”.
 
Proactive protectors, 17% of the population, “Fully 90 percent say they only share information about themselves online when they know how it will be used, and 76 percent clear the cookies stored on their browser. While they shop online because it saves them time, they are not willing to trade information about themselves to access deals”.