This article is part of our randomised, post-structural Let’s Build a Bank series of articles.
The rules of competitor analysis are changing as the economy moves from ego- to ecosystem. As customers increasingly own their own data, they also own and control the reputation of organisations, and applying the customer lens to our own and our competitors’ businesses is becoming central to understanding customer behaviour and attitudes. We are also moving from an “I win/you lose” towards an “I win/you win” world, where partnerships and complementary capabilities mean traditional portfolio control is less important to an organisation’s survival than before, customer stickiness is achieved an experience at a time and it’s impossible to fully predict how customers use your services. Organisations need to accept that the blurring edges between customers and employees is creating a need for a broader, community based management approach. In this article, we examine how shifting values are changing the lens and how we need to adjust competitor analysis, or partner analysis, to keep up.
Understanding the ecosystem – competitors and partners
Research into potential partners or competitors isn’t new, but it’s now both easier to perform and more important than ever. While corporate intelligence research into other organisations is often used for investment purposes, it’s also used to better understand competitive positioning or partnership opportunities. Many consultancies and specialists provide this type of research today. We’re old enough to remember when corporate intelligence, or a distant precursor to it, was more commonly known as Industrial Espionage and lacked the respectability of today’s consultancy-provided service. Information that was really useful to other organisations was hidden, meaning that underhand methods would be employed to discover it.
However the two concepts are fundamentally the same thing; finding out information about your competitors and potential partners, which influences your strategy and how you interact with them. And of course, now we don’t have to visit the premises of our investigation subjects to find information in filing cabinets; it’s available online, through a combination of official and unofficial sources. Official sources such as public filings, criminal records etc, may be subject to limited access, meaning that we may need to employ specialists to support us in getting the information, but increasingly organisations and individuals are finding it in their interest to make information about themselves that would once have been private, public.
This shift towards openness can be attributed largely to the changes to behaviour and in particular our attitudes to personal data, driven by the internet and changing expectations. Thirty years ago, few individuals beyond officially entitled persons such as shareholders, would have access to or much interest in the details of a company’s financial or strategic information, unless some helpful journalists had made it public because of some interesting data which they felt was relevant to the general public, for the public good. Nowadays we all expect a large company’s financial performance to be publicly available at the click of a button. The same applies to the background and history of senior officers in the company; we expect to see this on the company website, or on LinkedIn today, and it doesn’t take much to identify holdings in public companies, etc for any individual.
By contrast, any organisation that keeps information secret, is regarded with suspicion and is likely to be subject to social media commentary indicating it has something to hide. So by publishing information, organisations are less likely to be subject to suspicion and accusations – unless, of course, that information reveals some uncomfortable truths!
Such public disclosures are selective, subjective and managed by the organisations that publish them, so there’s still a role for specialists to investigate specialist, restricted or left-field sources. Searching for information in volume can also be time-consuming, and again the industry caters for the growing need for competitors and potential partners to be well understood, by the organisations in their sectors.
In addition, there’s now a lot of noise generated by “post-truth” news, opinions and the plebocratic accumulation of false, but clickworthy information about organisations which also needs to be understood. Regardless of whether it’s factual or not, a significant body of public opinion about any organisation will also influence its performance and reputation. Good corporate intelligence includes this in the analysis.
But what are we looking for?
Competitor/Partner Analysis
Traditional analysis tells us how well our competitors and potential partners are performing in a number of areas, enabling us to evaluate how well they are likely to perform in contrast to us and where there is a good fit for potential partnership. A broad analysis covers all elements of the business including its products, services, processes, distribution, customer segmentation and reach, strategy, key differentiators, management team and legal/regulatory compliance. It also enables us to apply the same lenses to our own organisation and understand how well we are performing in these areas, enabling us to make dispassionate comparisons.
As we’ve discussed elsewhere, traditional banking products are not differentiated. Most universal banks are pretty similar to each other; they offer the same product sets, the same sorts of services, have similar staff with very similar backgrounds and suffer from the same challenges of regulatory pressure, shareholder short-termism, inefficiency, technical spaghetti, legacy hierarchical culture, complex data and ageing systems.
So it’s odd that, historically, banks’ analysis of each other has not been particularly accurate. This is partially because of anti-trust rules, designed to prevent collusion such as LIBOR fixing between banks, but also, we think, due to a skewed lens that’s arisen precisely because there’s so little difference, leading to an exaggerated perception of differences. We have found, in our experience of a large number of universal banks, that many believe their competitors are more differentiated than they actually are. So a rational, data-driven competitor analysis can be extremely valuable, to highlight the (usually) small number of genuine differences, which can be exploited or addressed.
By contrast, fintechs and other potential partners for banks in the connected economy are usually radically different to the banks they are partnering with, so again a competitor or partner analysis can help to identify the strengths they can bring and the weaknesses that the bank may be able to support. However, this traditional approach to partner analysis breaks down when applied to new, innovative organisations and particularly to the SME Fintech, when we consider the different rules that apply in the connected economy. In the absence of relevant data on potential partners, we can instead look at them through two other lenses: innovation and customer perception.
How innovative are we? Our competitors? Our partners?
When considering why innovation is important, we’ll first provide our definition of what innovation means, as it’s a broad topic. In our view, innovation is not about cool new technology (although that might be involved); considering the innovations we’re seeing today in financial technology (fintech), very little of the technology involved is actually that new. Smart Contracts have been around for 20 years; bitcoin, which was a game changing technology, was developed nearly 10 years ago and the applications of distributed ledger are just starting to emerge fully. – innovation, in nearly all cases, is about the application of concepts, which may include technology, in innovative ways. That will usually involve evolution of the technology, but as the result of the use case rather than for technology’s sake, as we’re seeing today with both technologies.
Conditions need to be right for the technology or service to reach a market, and the creation of new markets is always in response to an anticipated need, rather than pushing a product for the sake of the product. Consider the development of steam power and ratchet-driven gears; both were pioneered by the Ancient Greeks, as evidenced by archaeological artefacts, and subsequently by individuals in a variety of societies – Leonardo da Vinci had some interesting ideas. But beyond a rather cool timekeeping instrument known as the Antikythera Mechanism, the public need or the opportunity to monetise and manufacture such things simply wasn’t there at the time; it took the growth of first global trade, faster communications and the need to navigate by night, that led to the development and refinement of clockwork, and, later, the growth of urban populations and manufacturing leading to the need for deeper tin mines in the UK, to commercialise steam power.
Successful innovation in business doesn’t usually need new technology; while fintech is about the exploitation of technology for financial services, that technology doesn’t have to be leading edge in itself, but applications of it, and the business models that support it, do, to reach the market and create new market segments. The technology behind QuickBooks, the app targeted at Uber drivers and similar micro-businesses, that allows you to scan a receipt and swipe right for personal, or left for business expenses, has been around for a while. So has the technology behind Uber; but it took the platform application of that technology to create the social shift that has occurred in peer-to-peer taxi rides and in the supporting ecosystem around the drivers.
So when we look at innovation, we examine every aspect of a business, with technology being just a subset of some of the components. We have used two models by Doblin (Deloitte) which help us first to identify where organisations sit in terms of how innovative they are, and then at where their key innovations are across a spectrum of innovation types. The first model helps us to identify where organisations sit in terms of their disruption to the competitor landscape; are they providing truly new products and services, or creating new markets? Or are they exploiting a parallel market, finding a gap neglected by incumbents?
This difference will help to identify the criteria by which we should evaluate them. A challenger in traditional markets will usually need access to all the traditional strengths listed in the last section; distribution networks are critically important to entering a saturated, or parallel marketplace. If that’s the case and we’re looking at the organisation from a purely parallel perspective, a more traditional approach to analysis will give us answers. However the more innovative the organisation and the more disruptive its offering, the more the ecosystem is changing many of those rules.
Figure BFB-ETB-CPCE-1: Doblin’s Competitive Positioning Matrix
Source: Doblin
For transformational organisations, it may make less sense to apply traditional rules, partly because there’s less to compare them to – for example an organisation creating new markets and defining new services, won’t have any direct competition, but the real question is whether that market exists and whether the service will be perceived as desirable to that market; in the absence of competition doing a simple SWOT analysis isn’t much use. Doblin provides a second model that can help us understand where and how they present true innovation in their offering; this requires some further analysis but enables us to identify where they threaten us by competing in new ways, or offer potential as partners. It’s also a useful lens to apply to our own organisations, which will help to identify gaps and opportunities, particularly when considering the opportunities offered by potential partners in filling those gaps.
Figure BFB-ETB-PCPCE-2: Doblin’s Ten Types analysis
Source: Doblin
In this model, we examine the level of innovation in each of the areas – as discussed above, most of this is not about technology per se. The example given is the scoring for hiveonline, which has some very innovative approaches, particularly in the platform interaction model, leading to a high network and product system score, whereas other aspects, such as branding and profit model, are more traditional. Because hiveonline is innovative in many ways, the scores are high across many categories, whereas a traditional bank which is moving to address a stuffy image, for example, would be likely to score higher on brand than in any other category.
These ten types follow a logical sequence from left to right, from a business strategy via its service and product offering, to its customer experience. By focusing on areas where innovative operating models can confer a distinct competitive advantage, such as the network and organisational structure, Doblin allows us to examine non-product and non-technology elements of operating models that also allow for innovation; consider the service and network model behind a platform such as Uber, or the structure of flat, non-hierarchical organisations giving everyone exposure to customer impact, as examples of innovation which is distinct from technology or product innovations, yet give organisations a differentiating market share.
As they also help us to compare and contrast organisations operating in the transformational segment, they also allow us to evaluate organisations with different, and untried, product sets or market segments, against each other and against a new set of standardised criteria for indicators of competitiveness, which isn’t possible when applying standard SWOT models. Of course, innovation isn’t the sole strength of any organisation, but by applying this lens across the full spectrum of criteria, it’s possible to derive some reasonable predictors about the firm’s behaviour in the untried, transformational space, and to predict in part customer reaction and takeup.
To use a couple of recent examples, an organisation with an innovative service, product system and network such as Uber, is likely to capture or create a market, assuming customer demand exists. In Uber’s case, they have carved out a new market from an existing market based on competitive pricing, but the main reason their astronomical growth has been possible is that the platform model is incredibly scalable, because they don’t need to own infrastructure or vehicles. Compare AirBnB, which has partially created a new market and boosted travel, to a traditional hotel chain; it takes months, sometimes years for hotels to fit out or build new property, but if you don’t need to own the property, that innovation means you can expand to fit the available market without significant investment or lead time.
Of course, innovation per se doesn’t necessarily translate to competitive advantage; you need to identify what the problem is that the innovation solves. For example, innovative configurations like these examples enable organisations to be extremely agile in responding to changes in customer behaviours, while flexible enough to scale rapidly. Innovation in customer engagement may, as in the case of hiveonline, present new opportunities for low cost promotion of services via partnerships and community support, rather than traditional push sales. By comparing types and levels of innovation, it’s possible to put together a partnership ecosystem map with a view to addressing particular strategic opportunities, as well as identifying where to learn and where to influence across multiple areas in multiple organisations.
Winning in the Ecosystem Economy
As we’ve discussed elsewhere, “winning” in the connected economy isn’t a simple case of moving to the top of the heap; when your competitors are no longer competitors as such, but part of your extended service model, it makes no sense to win at their expense, especially when considering that the network is stronger, the more interconnected it is. And conversely, your business suffers as your partners suffer. Simplistically, that means that instead of a binary “I win/you lose” position, it is possible to achieve an “I win/you win” equilibrium but at the risk of the converse, “I lose/you lose”, whereby the failure of a partner will translate to a negative impact on your business. While an ecosystem economy will help you to insure against this by creating a robust family of partners rather than relying on a single point of failure, it’s likely you will still end up relying on the robustness of some partners more than others, which could have a greater negative impact on your business in the event of a failure.
Figure BFB-ETB-CPCE-3: Winning in the connected economy
This means that, even more than supplier confidence, you need to have confidence that your partners, who are much more than suppliers in the ecosystem, are likely to succeed. But to succeed with you, rather than in spite of you. Hitting suppliers with punitive penalties can work when there’s no relationship between the supplier and your customer, but the impact of bringing customers closer to service providers is that they now share responsibility for delivery of the customer experience – and, more than ever before, no single organisation owns the customer experience.
Nobody controls how the customer behaves and the services they choose today, except the customer: in the ecosystem economy, the customer owns the customer experience. Customer experiences need to be designed in a modular way that works in combination with your ecosystem partners, so compatibility and shared values are more important than straight-through processing, while common vocabulary and quality standards trump data flow and process control excellence.
What do you look for in suppliers in the traditional economy? Usually, their track record of delivery, their solvency, the quality, consistency and popularity of their products, whether they’re people you want to do business with, and the likelihood they’ll go bust, alongside their price point, distribution and other practical external facing aspects of their business. In the ecosystem economy, however, while many of these factors are important, you need to evaluate them in relation to their impact on the customer, rather than to your supply chain.
Further, some additional criteria which you’d apply to your own business also need to be considered when your customers are your partners’ customers too, in contrast to the traditional model where their customer experience is controlled by you, acting as a buffer (and a filter) between them and your suppliers. Where traditional supply chain criteria are important to an old-world supplier relationship, their supply chain to you is irrelevant in comparison to their responsiveness to customer needs. As the ecosystem becomes the supply chain, you’ll be sharing aspects of customer interface, and you’ll need confidence you’re speaking the same language when agreeing how to deliver this.
You’re also likely to be providing some capabilities in the ecosystem which compensate for your partners’ ability to provide the same capabilities, just as they are providing capabilities you lack. For example, if your partner is providing advice to your customers, their direct customer support capability becomes much more important, but you’re providing the branding and distribution network, while you’re both using another partner for authentication and reporting, so their capability in this space becomes irrelevant.
Trial by customer
Big Data. We had to mention it sooner or later. Two of the reasons corporate intelligence is so much more important in the connected economy is that the possibilities are now much greater, and that it’s much easier to get it wrong. As we’ve seen, emerging norms always evolve through a learning process where we test and jettison false assumptions, and learn from surprising trends demonstrated by data analysis. It’s often said that nobody of our generation could have imagined when they were at university, that we’d have in our pockets a device capable of sourcing pretty much any information known to man, and that we would choose to use it to watch videos of cats.
People’s behaviours and how they interact with their environments and the products and services we offer is hard to predict; customer focus groups aren’t much good, because even customers aren’t very good at predicting how they’ll behave in real life, and sales people have known for years that customers will lie or embellish to impress the interviewer rather than tell the truth, even if the interviewer is a computer.
As we know, the very best predictor of future customer behaviour is past customer behaviour. Individuals’ behaviours are as unique as their signature, so they’re also a great way of validating identity, partly because customer behaviour patterns don’t really change. How your customers interact with your services in the future can best be predicted by how they interact with them today, but if you’re creating an innovative service, data can be hard to find. Correlation analysis can produce some strong predictions if it’s conducted by skilled analysts with comparable data, but can still be widely out when it comes to new offerings and services; a minor tweak in the operating model can cause customers to behave in very different ways and the relationship isn’t always obvious.
So when you’re considering partners, the easiest way to understand how well customers respond to their offerings and how the offerings are used is to look at their current behaviours and feedback. This is generally pretty accurate, but with two important reservations: it doesn’t give you complete confidence about how customers will interact with their service in the new partnership model, where you’re a visible part of the equation, and it doesn’t help you understand how new service offerings will land. This is where competitor and market analysis, looking at a broad range of comparable service offerings, can be a stronger predictor of an individual organisation’s service performance, assuming there are some reasonably comparable services, which again may not be the case.
Corporate intelligence can go beyond this by looking at other aspects of the organisation’s performance to predict customer reaction. If we look again at Doblin’s ten types, for example, comparing how customers have responded to the different approaches to innovation in startups can help us to understand which of these elements are likely to produce positive customer reactions – remembering that innovation doesn’t always lead to positive customer responses. Innovative approaches to customer engagement, for example, need to be examined at a granular level and with reasonably direct comparison across other markets, to understand how likely they are to be effective.
Having said that, past performance is generally a useful indicator of future performance. Key to building strong performers into your ecosystem, is understanding what is driving that strong performance; it may be the product, the service offering, the channel, the price point, or any one of Doblin’s factors. You need this awareness as you build in the partnership, to ensure you’re not jettisoning the thing that customers value, and retaining something that’s a hygiene factor or a detractor they’d previously been willing to overlook.
Where the customer experience is shaped by the customer as they select the services and components they want to use, and the more it’s customised, the less control you, or anyone else, has over how the products and services are used. This can lead to the customer creating a negative experience for themselves because they’re selecting a set of services which doesn’t meet their needs, or misses some elements that would suit them better. It’s out of your hands,
The challenges faced in today’s connected economy are different to those faced by traditional companies in the old, linear economy, where profits were driven by the ability to produce more of something at a lower cost and distribute it as efficiently as possible. Distribution systems have turned on their head, with viral network effects enabling tiny companies to get massive exposure, and small organisations with no inventory to achieve market valuations in the billions, outstripping competitors by using innovative platform models instead of trying to optimise an existing pipe model. Advertising has split into a flat-earth model, patching advertising into content just like old-style print advertising, and the more effective viral pull content, which we think will eventually replace it. Reputations are formed by customers and non-news, with organisations increasingly losing control, regardless of their advertising spend. In the ecosystem economy, your customers are your marketers, your distributors and your researchers and may also be your producers.
This is pretty scary for traditional organisations, and we’ve seen a lot of ingrained change curve behaviours within them as a result, with senior managers still firmly in denial that they’re now operating in a different competitive environment. This also causes friction and frustration in organisations, where usually more junior employees, who are closer to the customer, can see the need for change but are not empowered to make the change happen. It’s also hard for organisations to understand how to address the shifting paradigm holistically, so solutions to reputation and customer experience problems tend to be just as patchy and bolt-on as the solutions they’re finding to move into the digital age.
However, being in denial doesn’t help organisations move ahead. Banks are innovating around the edges, while employee behaviours, metrics and performance targets remain the same. In a world where your community is central to both how your organisation is perceived and your distribution network, there needs to be tight integration between how your internal resources operate and how your services are consumed – the edges of your organisation are blurring and you now need to be in the business of community management, rather than customer support and sales targets.
Customer values and behaviours also take on greater significance to your organisation and services, even when they’re not directly related to use of your services. So your network, your trust record and your responsiveness become the benchmarks by which you are judged, with what you would traditionally regard as product quality coming a long way down the scale.
Macroenvironmental Analysis – no business is an island
So far in this article, we’ve discussed the analysis and impact of a business’s operational structure and customer offering when considering it as a potential partner in your ecosystem, supplier or competitor and how your customer network is becoming part of your ecosystem community. However, every business carries with it the weight and the colour imparted to it by the environment in which it operates, which also colours your customers’ perception of both your own, and potential partners’ businesses and their offerings. How any business is perceived will be informed by your customers’ experience of and expectations of the type of business it is, by regional and country cultural biases and by other factors usually beyond the control of the business in question. There are various shorthands for this, which cover most of the same elements. We use PESTLE (or PESTEL for the U.S. audience), which translates to:
Political
Economic
Social
Technical
Environmental
Legal (and Regulatory)
But you’ll also see other acronyms (STEEPLE being one) which cover the same categories. And while on the whole, there’s not much that firms can do to change public perception of their organisations based on their perception of these aspects of the broader industry, where organisations choose to position themselves in relation to the overall positioning of the industry can have a significant impact; for example, some organisations choose to be more visible contributors to the community (impacting their social standing) or uphold strong environmental standards, as part of how they position their image and their stated values.
Where these are genuinely held values, embedded in the culture of the organisation usually because of the CEO’s personal beliefs – very few values get embedded without role modelling – they can be strong supporters of market position. However an unevenly applied policy, or a value system that’s perceived as window dressing by consumers, can backfire. So in measuring organisations against the wider background and their own positioning, it’s important to take into account more than just stated values and understand how well these are embedded.
Macroeconomic factors such as these can have a profound influence on an organisation’s ability to survive; those well suited to their environment are more likely to thrive whereas a poorly positioned organisation, however brilliant its offering, will struggle. Here’s a couple of examples from our own experience:
In the late noughties, a bank developed two major innovations in Eastern Europe – a wireless ATM and facial recognition for branches. Both were exploiting cutting edge technology at the time and using macroenvironmental factors peculiar to the region in which they were invented; the wireless ATM compensated for the country’s vast spaces and very poor communications infrastructure outside of cities, allowing the bank to use satellite technology instead of the unreliable and frequently disrupted physical communications cabling.
The facial recognition technology allowed tellers to get the customer’s details on the screen in front of them before the customer reached the teller; a solution that would have been unacceptable in Western Europe due to “big brother” fears, but was not only acceptable but desirable in a society that was used to assuming the state was watching their every move anyway. Sadly, neither of these really remarkable innovations made it beyond the pilot, because of the global bank’s (probably correct) assumption that they wouldn’t be accepted by customers in other parts of the world.
Scroll forward a decade, and we’re seeing vastly different attitudes to data openness in different parts of the world, again driven by cultural and societal differences. As we see the growth of big data analysis and the hugely varied global attitudes to data sharing, the very services offered by and shape of organisations taking advantage of these changes is different from country to country and region to region. Customers in Germany stick to cash because they don’t want banks to have information about their spending habits, whereas just across the border, Denmark is moving towards being a cashless society, while fellow Nordic states Sweden and Finland are seriously debating when cash will cease to be needed, because customers are so comfortable sharing their data and their spending habits.
Analysis reveals a correlation between the Nordic countries’ low corruption indexes, high levels of connectivity and superior education, but these factors are also true of Germany, so the attitudinal differences are, in this case, attributable to a number of societal factors beyond pure economics and technology, with historical factors providing a strong influence in this particular case.
So when you’re considering your potential partners or competitors, or indeed analysing your own service offering in the competitive landscape, it’s also important to consider the PESTLE
Conclusion
The old rules are rapidly changing as we move from egosystem to ecosystem and the edges of your organisation are blurring. You can’t afford to choose partnerships based on traditional values and measures and you need to unlearn much of what gave you certainty in the old world. As you consider future and current partners and the competition, you need to understand
How is your organisation and industry perceived and where do you need to position yourself to succeed?
Where are your capability gaps and which other organisations have those capabilities?
How can you both leverage partner capabilities and help partners with the capabilities you can supply?
How do you articulate your values and identify the values you share with partners or potential partners?
Who are your community members both inside and outside your organisation, and how can you help them reach goals based on common values?