Customer is king. And has been king ever since flint stone merchants competed for customers. Except for monopolistic market situations, the customer has always been in the centre of corporate moves. In the digital revolution the customer has neither been suddenly rediscovered nor was a successful entrepreneur in need of a reminder by business consultants promoting a new management fashion.
What has changed is communication technology so that a company is now enabled…
- to communicate much more efficiently with the customer, at a lower cost per customer transaction.
- to know the customer much better than ever before, if data can be captured and utilized.
- to utilize this know-how to find a better problem-solution fit, sooner and more often.
- to communicate differently with the customer, in different forms, in different contexts and at different times or situations.
Lets dive into each of those three factors one after the other.
First, why is more efficient communication with the customer such a game changer? So it’s driving down costs, what’s the big strategic deal about it? Let me explain that with a war story first. A couple of years ago I sat down with the CEO and founder of a major telecommunications group which had just bought the license to roll out 3G services, bought for over 7 billion Euros with many more billions to be spent on building the network. For him, all this investment in a network was just a necessary evil. What he really needed is meaningful customer service offerings that get’s customers to subscribe to his shiny new mobile network. As a consultant with a background in building internet marketplaces, I was invited to a series of one on one meetings to share my views. So I got to know him better from meeting to meeting and learned his dominant train of thought.
To my amazement, he was not much interested in getting to know the intricacies of built a well-balanced portfolio of services offerings or to get much in the details of each service. His prime metric was: What is the cost to acquire an additional Customer? Divide the costs to run a service with the average customer net revenue (that is revenue minus customer acquisition costs) and you have the number of customers you need to win for the service. Compare that number of customers to estimated total market size and if you arrive at a low number, you have discovered a gold mine, with lots of market share still to be conquered and reap profits.
Within this formula, the costs to run a service is largely given (it is the 3G Network itself), the revenue is either given by the market or – for innovative service up to guessing. Only the customer acquisition cost is really tangible for a company selling phone contracts.
This is the dominant train of thought of a founder and sales driven entrepreneur, which I have seen multiple times since then. Always calculating in his head average customer acquisition costs to estimate a break-even point and compare that to total market potential. This focus is extreme in the telecom or software industry, as marginal costs of servicing an extra customer are close to zero, but remains to be true in other sectors as well. The costs to gain an additional customer is the central cost factor in the telecom industry, followed by the costs of servicing that customer. These are the more or less visible costs of transacting with a customer, i.e. the costs to do business at all. If total transaction costs are higher than revenue, no exchange of goods or services takes place.
Now let’s have a look at the theory of transaction costs developed by 1991 Nobel prize winner Ronald Coarse. This theory postulates, roughly speaking, that all economic activity is just happening if the expected benefit – revenue or utility gain minus the costs of the transaction to the seller or buyer is positive. The seller’s costs of trading with a customer is not just the costs of the goods that change possession, its the cost to find a customer, to bargain and to ensure that he is paid. The customer has the same set of costs, she too faces transaction costs to find a seller, to bargain and to ensure that the good are actually delivered in the approved quality.
All market activity and structures evolve around transaction costs. This is the reason why in antique times market places have been invented, they are there because they lower transaction costs. This is the reason why an efficient legal system is so important in order for economic activity to prosper.
Transaction costs are the very fabric of economics – and now they are driven down massively by the use of information technology. This alone makes existing business models obsolete and opens new markets. In our days, its so much easier to do business with the customer efficiently. This alone changes everything, because one of the most basic variables of human economic activity has been changed for good.
This dramatic decrease in transaction costs is best visible in the development of eCommerce. eCommerce thrives because of low transaction costs. And these days really ONLY because of transactions costs, because the other two aspects of the customer centric revolution are far less developed, as will be explained below.
Second, the customer can be known better than ever before. A traditional shop keeper used to know it’s customers. She used to know what and when a customer bought, how an individual customer reacts to promotions etc. This relationship broke down with the emergence of supermarkets, a business model which is superior in respect to price, choice and aspects of convenience (parking lots etc.). Despite the efforts to introduce customer cards, the customer is largely unknown. Supermarkets know very well where to place merchandise for optimum sales, how to forecast sales and replenish on time by crunching numbers delivered by their store and ERP systems. But the customer itself is not an object of this number crunching, its just the sales figures and inventory numbers which are influenced by the customer.
Sales departments in todays companies are not really into customer analytics. Even Marketing lacks behind, because good customer data used to be so hard to find: Field surveys with at least some statistical relevance are costly and delivered ex-post, so that any findings might be attributed to the past and declared no longer to be relevant or actionable by the time they are available.
But now customers interact more and more via ecommerce, delivering a stream of data to merchants. They are paying more and more not with cash but with “digital” currency, e.g. credits cards, paypal etc. They reveal themselves through customer loyalty cards, through social media or through their browsers text and click stream.
This is a game changer, which’s potential has not been fulfilled yet. Just look at the offerings you get in your browser banners. All these advertisements are custom made for you, but how many are relevant to you? You will find adverts for things you or your kids sharing the device recently googled, sites you once visited or things you mentioned in social media or an free email platforms. But the logics of the banner adverts are, even so the likes of google et al invest billions, suffering from the problem to consolidate meaningful data from all kind of sources. All these data sources together would allow companies to not only offer things you where interested in the past, but to make a prediction of what you are interested in right now. The challenge is not making an accurate description of what the customer once did, but what he will do next.
The holy grail of communicating to customers is to look at the customer’s past, estimating her actual situation and anticipating whats on her mind at this point of time.
Smartphones are solving a lot of the data source problems. They are delivering a stream of truly personal data with a lot of context, including geographic data, in real time. More and more apps are contributing to survey the customer all the time, e.g. Shop kicks app, if permissions are granted. We definitely are on the way towards a whole new level of understanding of the customer. But we are not there yet. If you want to get a good first person experience of personalized customer interaction, try the like of US based Trunkclub.com or European clone Outfittery.com, two curated shopping sites for your next fashion purchases. They are not quite there yet, but definitely belong to the leaders in the field of understanding & servicing customers better.
The third point: Understanding the customer and monitoring his needs enable a company to build better products and services; in other words better fitting solutions to the problems a customer faces. Finding the right Problem-Solution fit is the key challenge for the initial phases of every start-up. As a start-up needs to build a product before scaling it’s needs to constantly experiment with the product, iteratively adapting according to customer feed-back, scrap functions not valued and come up with new, often surprising ones. This culture of experimentation is rarely existing in todays companies (and if existing limited to Research & Design Departments).
Let’s take the real example of a fashion retailer company running over a thousand stores. This fashion retailer creates new products, a new collection every month. Constantly new fashion items arrive at the stores, theoretically speaking new problem – solution fits. All fashion products are actually quite similar. e.g. a shirt can be described by color, size on a rough level. That is the usual material master view. Beneath that are attributes to describe the shirt, such as material composition, fit, neck type, print, applications, buttons etc.
Every month, critical decisions need to be made: Which items to buy, given a 6 month delivery time? How many items? At which price points? Which composition of items, i.e. relations of shirts to trousers etc.? What to deliver to the stores right now, given current weather, events, stock levels, degree of promotions in the market? Where to place which merchandise when? Historically, fashion retailers rely
- on historic data to make these decisions, e.g. Which item sold best last year? This is all list & excel table based.
- on best practice rules build over the years, a repository of accepted business practices which became accepted as common knowledge regardless of the origins of these rules
- on a lot of subjective judgement about the market, gut feeling, which is definitely necessary to make this or that bolder move and differentiate from competitors
- Forth, at the end the highest paid person in the room usually has the final call.
With digitalization, there is a chance to come to a better problem solution fit is a more precise manner. While it is notoriously has to predict if a past bestseller will be a future one, as minor alterations of the product, the price or changes in taste over time – the problem/ solution fit – have large impact on item sales. But it has been demonstrated that forecasting based on the 50 or so product attributes (size, fit, color, material, applications etc.) yield good results, increasing sales (see Fisher, Raman in The New Science of Retailing, Harvard Business Press 2010).
Problem is, in todays decision processes, this data is not used. The benefit is clear, the data is there, all that is required seems to be the IT power to crunch the numbers and the clever brains that set up the algorithms. The result would be a generous increase in sales. But it is not happening. Why?
- Scientific forecasting is a statistical excursive and the result is a statistical one. There are significance levels, standard variances, correlations, co-variances. Most decision makers of all levels have a problem to grasp these concepts.
- Decisions are made under uncertainty. E.g. our forecast predicts there is a 85% chance to sell 20.000 items of this shirt. But forecasts maybe wrong, especially if build on new algorithms and interpreted by people who are still learning. Learning takes time, errors will be made. Is the organization willing to go through these period or revert to “proven” practices?
- Blending objective forecasting results and subjective, gut feelings is an art. More than that, according to Forecasting guru Nate Silver (see sources) it is the most important art in forecasting. Subjective Feelings are important. Objectives Forecasts too. Only if a rigorous process of discussion, of weighing facts and human imagination, can be established and adhered to month by month results will increase. It is a process of rehearsal that eliminates biases, as both “objective” and “subjective” data is not always right. And it is nerve wrecking for humans to accept in a business environment that each time a decision is made a die is cast. There is no certainty about a result, but there is probability.
To finish up this war story: Now imagine the data available on product performance can be augmented by feedback by customer customer panels via social media or specialized agencies within hours, prior to making decision on the procurement of a shirt or the design of a shirt. The technology is there to come up with ever better problem – solution fits. The results have been proven. Alas, our work organization, ability toward with big data and finally cultural abilities lack behind.
The fourth point that has changed in the relationship to the customer is that different forms of communication are possible. Customer communication today relies on the store associate helping an anonymous customer, the cashier collecting money, a company broadcasting a more or less undifferentiated message via advertisements through any form of media, or a company sending a shot gun style message at customer needs by sending a slightly personalized newsletter. Thats as far as it gets today.
Plus, there is social media, where brands try to convert customers to followers or use it as a customer service outlet. The problem in using social media is to be relevant to a customer. Most brands are simply not relevant enough for customers to dedicate time to. Few dedicate time on a social media platform even to her favourite brands. Even iconic brands, such as Harley Davidson or Burberry struggle to gain sufficient time with the customer to build a meaningful relationship. That does not mean social media is irrelevant. It is strongly relevant for growing parts of the customer base. But high quality presence in various social media is just a basic factor to do business, more like a hygiene factor. It is only negatively differentiating, i.e. if done wrong, amateurish or not at all: Business will suffer.
The holy grail of communicating differently with the customer is about speed and accuracy. As over time the customer knowledge challenge is better and better solved a company will know in which situation a customer is in and will be able to deliver an accurate proposal right in time. If a company gets the situation, the timing and the accuracy wrong too many times, the customer will close down the communication channels and create a window of opportunity for competitors to come in and fill this gap. The customer may still like the product, but will dislike the spam the brand throws on her and will disengage more and more. She may still buy, but a company should not expect to earn an extra margin as a brand premium.
A glimpse how meaningful communication to customers may be orchestrated in a on time, accurate and personal meaningful manner is this message received on my mobile phone by Outfittery.com:
The essence of customer centricity is combined in this example: Efficiency, knowledge and a different way of communication. All aspects build upon another. If all three are combined business excellence is achieved.
Even when sufficient customer data is available, the trouble is how to make sense of all the data and send a meaningful message in real time to the need of the customer. Et voilà: Enter the realm of Big Data & enter the realm of the next blog post.