Why do your customers choose you over your rivals?

A man walks into a bar …

The bar chain carries out some customer analysis. Customer Effort Score (CES) tells them it was easy for him to get served. Customer Satisfaction Score (CSAT) tells them he was satisfied when he left. Net Promotor Score (NPS) tells them he has a positive perception of their brand. Market analysis tells them about the suppliers, logistics and brand positioning of the two rival bar chains on the same street.

But none of these metrics tell them why the man walked into their bar. And they don’t tell them why, next time he fancies a drink, the same man chooses the bar next door. Or what they could do to change that.

Relative Attractiveness vs. old-school CX analysis

CX performance is always relative. How successful you are at attracting customers is relative to how well other market players are doing the same thing.

Effective competitor analysis should empower you to make strategic moves that influence customers to choose you more often, winning you market share from rivals.

Current CX analysis fails to deliver critical context on competitors and how customers choose between alternative solutions. It leaves us blind, because it is too far removed from the customer decision-making environment.

Relative Attractiveness (RA), Uncrowd’s approach to competitor analysis, solves this.

RA recognises that the primary driver for growth is how attractive your proposition is to a customer relative to your competitive set. The competitive set is most usefully defined by customer story – what the customer is trying to achieve, in what needstate and mindset.

RA measures the customer decision-making environment, comparing your proposition to your competitors’ over specific customer stories. It’s a gamechanger.

Shortcomings of current competitor analysis tools

Current competitor analysis tools do some jobs well. They assess the status of rivals, suppliers, buyers, substitute products, and potential entrants, providing insight that tracks a competitor’s moves in the market, eliminating surprises and providing a basis for strengthening market position and growth.

But current competitor analysis involves making sense of ambiguous, multidimensional market and operational data, typically incomplete or incomparable at source.

And while conventional competitive analysis facilitates a high-level tracking of market movements, tactical execution against these factors can remain a disconnected guessing game, or a never-ending, costly, cycle of test-and-learns. So, much investment falls flat as a result.

Traditional competitor analysis is too distant from customer decision-making

The challenge with current competitor analysis tools is that they are far removed from the point of customer choice – the moment when a customer chooses you, or a rival. And choice of retailer rarely happens in-store, or even in-town or in-browser.

Effective analysis should deliver accurate descriptions of how a customer will choose for a particular customer story, and what a business can do to influence that choice. It should consider the behavioural science of human decision-making.

Current industry sector analysis and macro-environmental analysis struggle to deliver this insight because those forces are far removed from that moment of decision. Worse, current analysis always lacks equitable data across the competitive set. You have your own deep data but replicating the same in a rival’s business has up until now appeared to be impossible.

The future of effective competitor analysis is at the level of customer experience (CX).

Current CX analysis tools are flawed by cognitive bias

Current CX analysis relies heavily on customer surveys to evaluate the customer decision-making environment. Customers are asked to report on their own decision-making and predict future decisions. Growth strategy is built on interpretation of the data from such surveys.

But whilst customer surveys can be effective at uncovering operational issues, they are inadequate to the task of evaluating a complex decision-making environment with multiple competitive options.

Humans are poor at analysing our own decisions. A string of natural cognitive biases mean customer surveys cannot deliver reliable insight into the customer decision-making process.

Such cognitive biases include:

1. Heuristics - answering complex questions using simplifying operations
2. The Fundamental Attribution Error - failing to recognise the importance of context in decision-making
3. Confabulation - the unintentional creation of false memories
4. Misattribution - wrongly attributing the source of an event
5. The Misinformation Effect - memories being influenced by subsequent events
6. The Intrapersonal Perspective - inability to predict how we’ll act in future when feeling different
7. The Intrapersonal Retrospective - inability to accurately analyse why we acted the way we did when we were in a different mood
8. Status Quo Bias - preference to maintain an existing choice even when ‘obviously’ superior alternatives exist and are known
9. Hindsight Bias - viewing past events as more predictable than they really were
10. Choice-supportive Bias - remembering the choices we made as better than they were and remembering the options we didn’t choose as worse than they were

(See our Further Reading section for more information on cognitive bias.)

These natural biases undermine the reliability of customer survey data, the established tool for analysing CX.

Current CX analysis tools are restricted by the nature of their metrics

Customer perception feedback metrics are dogged by other fundamental problems:

• High data volume required to deliver meaningful insight
• Vulnerable to gamification by staff
• Dominated by negative bias from disgruntled customers
• Lacking in actionable detail
• Questionable subjective input
• Reporting point-in-time responses rather than long-term loyalty
• Lack of connection between perception insight and commercial application
• Inward-looking, using data from within an organisation, ignoring competitive influences.

So, what’s the alternative?

The Alternative: Relative Attractiveness (RA)

The Relative Attractiveness methodology is competitor analysis rooted in the customer experience. Relative Attractiveness measures your proposition against other competing propositions, over specific customer stories. Because attractiveness can only be represented through evaluating the CX environment, RA measures the decision-making environment at a customer level. RA treats CX as observable, measurable and quantifiable.

The Relative Attractiveness model takes a fundamentally different approach to competitive analytics. The underlying principles of RA are:

• The primary driver for growth lies at the level of customer experience.
• Even strategic top-line items such as proposition, positioning, channel strategy and so on, are fundamental shapers of customer experience.
• The competitive set is determined purely by the customer story – the solution sought, combined with the mindset of the customer.
• The appeal of a market player as solution provider varies over time for any given customer and is dependent on the customer story.

It's important to note that we are not suggesting customers are necessarily aware of making these comparisons between solution providers. Kahneman and Tversky’s work on ‘two-system thinking’ is just one accessible example of behavioural economics describing human decision-making as a combination of overt and non-overt influences. Thayler’s 1980 work Toward a Positive Theory of Consumer Choice is another. Biases and heuristics must also be considered, and RA does this at the base level.

Relative Attractiveness simplifies competitive analysis

With a Relative Attractiveness (RA) approach, the relative position of each market player is directly related to how successfully each business presents its solutions as the optimal choice. It measures that which is present in the customer decision-making environment.

This simplifies the required data inputs, allowing us to bypass incompleteness in our analysis by removing the need for non-public, proprietary information, avoiding non-equivalent data sets between competitors.

Competitor analysis at customer decision level

The competitive set for analysis is determined by customer story. Within that story, relative attractiveness depends on the mindset and needstate of the prospective customer in the moment of decision. Context is crucial because it overrides rational decision-making. A decision we make in one context might be different in another context.

Context when considered within RA can also override traditional two-dimensional segmentation. In traditional segmentation, an assumption is made that an individual customer only ever shops in broadly one mindset, unaffected by the context in which they shop.

A man walking into a bar might be segmented as “over-50s craft beer enthusiast”, or by other arbitrary segmentation traits; age group, affluence, education, the car they drive and so on.

But when the competitive set is determined by customer story, the values within that set are not fixed but fluctuate depending on context and mindset. The man walking into a bar might make different choices for a lunchtime pub visit, ordering coffee and depending on a fast lunch menu. If that customer is in your craft beer segment, you might not even talk to them about the importance of coffee and food service speed.

Relative Attractiveness and market-share change

A core tenet of the maths around attractiveness is the idea that attraction is always relative within a market. (Attractiveness in this context refers to how alluring a proposition is to a customer – not to be confused with Market Attraction, the established discipline of assessing whether a market is attractive for launch.)

To be more attractive requires that alternative solutions be less attractive. Increasing one’s own attractiveness relative to competing alternatives, and successfully communicating such changes over time, should therefore logically lead to gaining share of that market from those alternative competing options.

Thus, relative attractiveness becomes a straightforward describer of likely changes in market share. Your increase in RA always comes at the expense of someone else’s, leading to increases in your market-share relative to those others.

This is particularly important in shrinking markets in the current period of economic hardship when new customers aren’t entering the market and no new spend is available. Winning market share in a shrinking market must always involve taking customers from a competitor.

Using RA, rate of market share growth is dependent on your navigation of attendant biases such as the status quo bias and ability to manage customers’ natural loss aversion.

Contagious improvement

One significant benefit of measuring RA is that when you know what people really value in your customer experience, you can build advertising, comms and CRM content that talk about those things. When you know what really makes you relatively more attractive, you can more easily shout about that.

The CX insights delivered by RA provide a much better guide on making communications activity effective. It’s not a stretch to imagine creating Bass Contagion Curves to describe rate of change following improvements in RA.

Which bar does a man walk into?

Traditional competitor analysis has its place, but the gaps in data and understanding it leaves require a new metric to fill.

RA is that metric.

There are three bars on the street, and the man thinks he chooses this one on a whim. But really it was the large windows, the obvious free seats, the lunch offer on the board outside, being in the mood for a quiet coffee alone with the paper.

Armed with this information, the bar chain would know which variables are likely to influence the man to choose them again next week, and the week after that.

Using RA as a tool, organisations can gain an understanding of their position in the customer decision-making environment, relative to their competitors.

A business’s CX attractiveness can be enhanced by leveraging empirical findings of decision science theory. RA provides the data and analysis required to set growth strategy, allocate budget and make changes that influence customers to choose you over a competitor more often, the key driver for market share growth.

Practical application of RA

RA can be managed and increased by answering five questions:

Question 1: What is our RA and is it trending in the right direction?

Question 2: According to RA across the customer journey, where are we winning?

Question 3: Where are we losing?

Question 4: Where along the journey do customers drop out?

Question 5: What are the jobs to be done to reduce purchase friction and increase buyer reward in the places identified in 2, 3 and 4 above?

Want to find your Relative Attractiveness? Talk to us to book a chat and a demo of the Uncrowd platform.

Further Reading

Zahra, S. A., & Chaples, S. S. (1993). Blind spots in competitive analysis, Academy of Management Perspectives, 7(2), 7-28.

Fleisher, C. S., & Bensoussan, B. E. (2015). Business and Competitive Analysis: Effective Application of New and Classic Methods. Pearson FT Press.

Gordon, I. (1989). Beat the Competition: How to Use Competitive Intelligence to Develop Winning Business Strategies. Basil Blackwell Publishers. Retrieved 6 27, 2022

Porter, M. (n.d.). The Five Competitive Forces That Shape Strategy. Retrieved 6 27, 2022, from The Harvard Business Review: http://hbr.org/2008/01/the-five-competitive-forces-that-shape-strategy/ar/1

Darley and Batson, From Jerusalem to Jericho, Journal of Personality and Social Psychology Vol.27 , 1973

Lee Ross, Teresa Amabile, Julia Steinmetz, Social Roles, Social Control and Biases in Social-Perception Processes, Journal of Personality and Social Psychology Vol 35, 1977

Christian Rudder, Dataclysm: Who We Are When We Think No-one’s Looking, 2014

David Hargreaves, Jennifer Kendrick, The Influence of In-Store Music on Wine Selections, Adrian North, Journal of Applied Psychology, Vol 84, 1999

Linda A. Henkel, Mara Mather, Memory attribution for choices: How beliefs shape our memories, 2007, August 2007, Journal of Memory and Language

Ryan Carlson, Michel Marechal, Bastiaan Oud, Ernst Fehr, Molly Crocket, Motivated Misremembering: Selfish decisions are more generous in hindsight, 2018

Richard Shotton, The Choice Factory: 25 Behavioural Biases that Influence What We Buy, Harriman House, 2018

David E Bell, Ralph L. Keeney, John D. C Little, A Market Share Theorem, Massachusetts Institute of Technology, 1974

Adam Kucharski, The Rules of Contagion: Why Things Spread - and Why They Stop, Wellcome Collection, 2020