I was thinking the other evening about the various types of business models and especially on the nature of lasting competitive differentiation (yes, I know, I really should get a life). It's all the fault of Tom Davenport's writings on 'Competing on Analytics', I keep getting inspired by it.
So, here is what was going through my mind: traditional thinking seems to imply that, presupposing you have something your customers value, competitive differentiation seems to stem from three major areas:
- What you have/offer is unique, impossible for your competition to (quickly) copy or mimic (typically of R&D centric organizations) or,
- You are simply the best at it - your product or service is just miles better than the competition and customers are prepared to pay you for the difference or,
- You are the 'low cost' supplier - the cheapest, the one customers turn to when they can't afford unique or best.
So, if a company decides that its business model is going to be based around being cheapest, it will relentlessly drive out cost from all parts of its business - from purchasing, internal processing and distribution and sales. The additional costs of 'primary' R&D or 'best in class' are probably seen as too great to allow them to also be the lowest cost supplier. Now, I appreciate that this is simplistic and many organizations try to have some coverage in 2 or more of these areas. Nonetheless, most business schools will tell you to choose which of these you are trying to be and then focus on that.
But I disagree because, whilst it might have been true previously that being affordable and best-in-class were inimical, Business Analytics, combined with modern processes and systems, allows you to leverage an asset you already have - data - to create additional business value.
Here are some examples:
- Analysis of customer behavior allows you to tailor their experience by only focusing on what they really want - you save money by not wasting marketing on unwanted customer communications, they have a best-in-class experience because it feels personalized and, persists across channels
- By understanding and predicting risk you can make better credit decisions - extending credit lines to maximize revenues without overextending your risk to loan defaults
- Performance management analysis and forecasting allows for increases in the quality and value of your internal processes whilst also driving down the cost.
- Customer analytics with production forecasting and 'short run' manufacturing allows for personalized products at close to 'off the peg' prices.
- I could go on and on...
So, business analytics can be at the heart of a new unique - the ability to be best and affordable and best of all, it allows you unique insights into your supply base, your own organization and market/customer behavior - an insight your competition can't have.
4 Comments
That was an excellent post with some great information.
Bargain hunters … I’m not sure whether we should love them or loathe them but if you’re in business you’re certainly going to have to deal with them. They’re out there in just about every vertical and these days there seems to be more of them than ever.
They’re always looking for the cheapest price and they’re always either asking; “Is that your best price?” or “I saw that cheaper in the store round the corner.”
Sadly you’re never going to convince some of these people that price is not always the one factor you base every single decision on. There are many other factors that can be far more important than price alone and it’s these factors that can be your defense against all but the most rabid bargain hunters.
Hello Stephanie...
...and thank you for the comment.
I agree, there are always those people who will try and negotiate a rock-bottom price: anyone who has met with a purchasing officer will know that is how they get their jollies - by getting a large discount out of a vendor.
But here's my point - it doesn't matter how cheaply you offer your goods - someone, somewhere can do it cheaper (even 'buying the business' occasionally). However, if you are the only game in town (unique) or offer a competitively different level of goods/services (best), you are in a much better position to protect your price premium.
For example, I my local supermarket, but I shop there all the time because it is cheap and convenient. What I never do, is look for reasons to go there if I don't have to. But I one of my favourite stores - all the people there know me, they know my preferences and what I like to buy/be told about. I know they are charging a bit more than average, but I value the experience enough to pay the extra. That said, I do shop elsewhere when I am looking for a bargain - but what if my favourite store could offer a similar level of service at discount prices? Why would I ever want to go anywhere else?
I agree with your analysis but have a few questions.
Do large companies with loads of data have a competitive advantage over smaller regional companies? ISO reports that there are one third fewer auto carriers since the advent of credit scoring.
With a reported 40% of CIO's concerned about quality data shouldn't the accuracy of current business analytics be questioned?
Will the use of segmentation analysis price some good customers out of the market. Better job equals better price.
Business analytics is a powerful tool and hopefully will be used to the benefit of the companies that use it and all their customers.
Bob Rojas
Hello Bob and thanks for your comment - I will try and answer each of your points in turn:
1. Often it is not a question of of data - : in fact, having lots of bad quality data is definitely a disadvantage - it mean you are making decisions without knowing the true facts. That said, all other considerations being equal, I would say that more is better, because it gives you more choices. That does not mean to say that there is no role for smaller companies, who can often differentiate on the basis of niche (unique) offerings or on a deeper level of customer intimacy (but see my original argument in this post - lots of customers customer intimacy is a powerful mix).
2. Absolutely, accuracy is vital. It starts with the data (see 1. above), continues through selection of analytical techniques, and carries on into the ability of the organization to interpret the results. Finally, closing the loop with feedback has to be built in so that the organization can refine and tune their models. When an organization really appreciates what role they want business analytics to play, it becomes relatively straightforward to understand what capabilities it then needs and how to acquire them. Business Analytics is not a 'magic bullet': it supports creativity in business decision-making, not replaces it.
3. Segmentation is neutral - it just allows you to understand the customers better and match them with the appropriate products, services or price. It's like credit scoring - it should not disenfranchise a customer, but neither should it expose a financial institution to unexpectedly higher risk. Simply put, if you are more likely to default, the institution carries a higher risk and therefore has to charge more to cover their exposure - I don't think I need to point out what happens when you don't get this formula right. All financial institutions accept risk (as do we all) - the just need to accurately put a price on it. The challenge comes when no institution wants to carry the risk (because it is unacceptably high or uneconomic) - that's when a different model has to take over (e.g. state support for health care insurance for the elderly).
4. I agree, I think it is better to base decisions on fact, rather than gut-instinct or relying on past experience to forecast the future. There is plenty of evidence to support this case. In the end, you have to act on what you know, but if you know nothing you are leaving the results up to blind chance.