How an uplift in CXi scores can deliver remarkable benefits for your business

A recent Forrester report looked at companies that had recently improved their Customer experience rating from negative to positive looking at whether their customers were likely to consider another purchase, to switch suppliers, or to recommend their current supplier to friends, The findings demonstrate in no uncertain terms that there is significant achievable upside to be had from improving the customer experience.

The trend of revenue uplift for any company that has improved its customer experience is not only strong, but still rising demonstrating that customers place a lot of value on this and are prepared to reward a supplier that makes an effort,
The reduction in customer churn continues to be significant, but this trend is falling slightly.  If this slight fall is statistically significant then it may well be that once an acceptable level of service is reached the value of further improvement is greatly reduced. After all, for many suppliers, all we want is that they actually supply and they remain invisible.

Chatter is rising rapidly. Both recommendations and the opposite are on the increase.

The impact on recommendations is very significant and growing when a supplier improves customer experience, but conversely the impact of falling service levels is equally damaging the supplier as a result of negative chatter

Increasing CXi scores from below average to above average had the following financial benefits. As you can see, the benefit is greater in some industries than others, but is very significant even at lower levels.

 

Industry Customers Reduced churn Further purchases Word
of mouth recommendations
Airlines 80m $807m $555m $56m
Credit card providers 61m $427m $368m $30m
Hotels 44m $380m $320m $29m
Retailers 67m $273m $237m $62m
Banks 15m $73m $81m $7m
TV companies 17m $92m $47m
Consumer electronics 10m $40m $43m $20m

Source: North American Technographics® Customer Experience Online Survey, Q4 2013 (US)

 

For many industries there is a ceiling whereby customer experience improvements will no longer result in revenue or profit upside. The obvious ones are products we buy very much on Price point.In these cases  we are happy enough with what we are getting and not willing to pay any extra under any circumstance. For example, to return to cheap flights. My favourite airline leaves on time and arrives on time at least as well as any other and charges me slightly less on average for an equivalent service. If this airline is able to reduce waste somewhere, then perhaps they can afford to improve service, that’s all well and good, but if they start spending on it, then sooner, or later prices will go up and I’ll go elsewhere.
The most thing these companies can improve at no cost is the attitudes of staff. A smile costs nothing it is a way of thinking and a culture, nothing more.

 

Imagine if you are running a business where you compete with a number of others in a controlled industry and you all buy the same thing in the same place at the same cost and sell it in the same market at more or less the same price. There is nothing to compete on. The only chance you would have to differentiate would be in how you treat your customers when they have a problem and how you manage customer acquisition and customer churn.

Imagine if you never spent another penny on advertising or sales commissions, kept your customers for their lifetime and had them all recommending you to friends. Now that would be a hell of a proposition.

 

 

Using personalisation cleverly to grow your customer relationship and keep your sanity

In my next  instalment I plan to look a little more closely at the broader context of recommendation engines and how they might be used but foe now I will simply point out that they are broken into two broad types Content Based and  Collaboration Based. The first refers to product knowledge and the second to behaviour of customers. Today I am only discussing the latter type

There’s two fundamental types of customer information used in recommendation engines and customisation strategies: 1. Real time knowledge of customer in context and 2. Historical knowledge of people with context.

I like to refer to them as 1. Personal and 2. Collective.

The first problem you run into when you start an ecommerce store is that you don’t have any historical data to start from. Making decisions about “people” is another way of describing prejudice. Of course all prejudice is not bad, we help old ladies across the road and we are kind to children. But when somebody hears my accent and offers me a particular brand, I am irritated.

What I find especially infuriating is when I simply cant find what I want on Google because it thinks it knows what I really want based on the fact that I looked for it twice last week.

The point I am making is that people are individuals, they change their minds, they like variety, they find what they are looking for and loose interest, in fact they really do not want to see what they just bought on offer at a better price.

The bottom line here is that relationships have always been difficult even when you are at the next desk or sharing a home, let alone seeing a cookie form the same machine create different click streams and view different content.

Personalisation and making recommendations based on algorithms can be high risk and must be done with care and forethought.

 

Context

Context is a critical factor in storing and classifying interactions for recommendations and personalisation.  When I go in our local store the shopkeeper who knows me very well takes into account the day of the week, time of day, look on my face, which shelves I’m looking on and what he remembers about my usual purchases and he then decides whether to let me walk out or to ask if he can help. He rarely gets it wrong.

If he did get it wrong very many times, I would be very tempted to walk a few yards extra and shop anonymously. Why should I have to explain myself?

personal1

Imagine if he wiped lots of stuff off the shelves on the basis that I won’t be interested in them today, but filled the shelves with what I bought on this day last week, or on my last visit.  Not very good.

Imagine if I run a social network, I know a great deal about where you are going, what you like etc. If I provide your phone network, I know who you call, and receive calls form, when you use it and how long for, when you never use it and when it is off and where you are right now. Maybe I see your calendar and your emails too? Not to mention half a dozen spook outfits eavesdropping.

With this kind of information I could be very invasive, so it is critical to only use this type of data with the customer’s consent and to put that customer firmly in control in case they should change their minds. The way to achieve this is to offer them value in return for their data.

 

In a typical e-commerce implementation you will have access to the following types of information:

Purchases,  wish list adds, cart adds, views, shares, favourites, downloads, shares, watches, likes, follows, where they were referred from and you may know about the search terms they used to get to your site.

You may also know about the sites these customers visited after they left your site when they have been active, their spending patterns, whether they buy for children, their gender, devices used, devices ordered from, devices emails were read on, where they have browsed from geographically, and a whole lot more. Once registered you could collect data on their social media activity.

If you are an OmniChannel retailer, you will have more data in your CRM, your ERP, and other places

data sources

This information exists in many places: In your customer database, in the web server log files, out there on social media sites, on Google, in an advertising network you work with,

 

In a typical solution, the information might be classified in three ways: 1. Customer , 2. info and context 3. Offer.

e.g. you create facts such as:

Customer, info,  (info context), Offer.

John Brown, likes(3) (June 4 14, Saturday, mobile ), gizmo product

John brown, price search(June 6 14, Monday, PC), gizmo product

 

Each piece of information is about a specific offering or product and it relates, of course, to an individual customer. In addition to that basic information, it is invaluable to collect context.

Understanding that Mr Brown never orders on his mobile, but does look at info helps you decide what to present to him. Maybe the solution is to offer him a convenient app that makes ordering easier. Certainly if 80% of his browsing was on mobile devices and 100% of ordering was on a PC,, this would be a strong indication

The way you might arrive at this type of conclusion would be to query these stores of facts, summarise what we know and make inferences.

This is a lot like data warehouses where you store vast flat tables, analyse them once then stores summaries in a more accessible system.

 

e.g. John Brown, likes(3), gizmo product  That weighted his interest at 3

Two days later he returned infer a higher interest, say 4

He asked for pricing info, this puts him at 8 or thereabouts

So we can summarise it as ; John Brown likes (June 6 14),gizmo product (8)

We know that he orders via PC, that was not a barrier.

We know that seeking price info is a sign of desire and conviction so why did he not order?

We might assume the price was too high.  Perhaps we are all too aware that we are not the cheapest in this market and we rely on positioning to overcome price objections.

We may decide to send a once off special offer on Thursday morning, so he cant resist because he frequently browses then so he may have free time on Thursdays.

That is an example of generating a “Next action” on the basis of knowledge and intelligent inference.

Of course it could be that he planned to order on Friday at the full price, that is a price we either do or do not decide to pay for John Brown’s business. Eventually we will know enough to be able to predict this accurately.

If we knew that he was a highly influential blogger with a large following, I wonder would this influence our decision?.

bigdata

Contrary to what is written by cloud vendors and Hadoop junkies, this kind of data and that kind of information is rarely dependent on petabytes of data spread across numerous clusters. It could be done with a basic RDBMS, though it would probably be easier with an RDF store downloaded for free.

Don’t get me wrong, there would still be some development to do and it wouldn’t work till a bit of data had built up, but you don’t have to be Google or Yahoo to benefit from this technique.

 

An interesting side effect of this approach is that the longer the customer keeps visiting the bigger investment you have in keeping her, because you know exactly how to twist her arm. Her customer lifetime value is infinitely greater even at a discount. Perhaps eventually, these relations will be reflected in the price each individual customer is asked to pay.  There’s a thought to conjure with.

What if John turns up at your Branch in Oxford street with his decision made and ready to order? What if he orders online but wants to collect from your store in Paris next week?  What if there’s an issue after delivery and he wants to get help form your store in Las Vegas while on his travels?

What if the cart tells him “sorry, we’re out of stock” and then he walks to the shops and sees one in your local store?

I know just how difficult these problems can be and how expensive it is to overcome them. It requires not just a technology rebirth, but a culture change like no other.