Because time-based competition increases pressure on constrained resources, What to do first? is a more important question to answer than what to do? Beyond the usual attractivity vs difficulty matrix, two other dimensions are important to take into account to prioritize data-driven value creation opportunities.
Data and analytics open for many different value creation opportunities. Either related to revenue increase or cost reduction, use cases, and examples are numerous:
- Increase in sales by a better recommendation algorithm
- Increase in repeated buying through customized marketing
- Increase in turnover by customized pricing
- Develop a new business line selling data and data services
- Automating tasks through machine learning
- Reduce errors and risks through better predictions
- …
When I work with clients on identifying value creation opportunities, it’s usual to end with a 10-20 items list. Of course, they cannot be implemented at once and one question arises quickly: What to do first? And right after, another follows: What to do next? And then on again.
It’s all about planning.
Why planning?
There is a long and intense debate on the vertus and limits of planning. On one side, planning is an efficient way to allocate resources at scale in large organizations, and on the other side, critics argue that when it comes to new initiatives, the reality of the development differs massively from the initial plans.
Both are true and they may not be as contradictory as they seem. One sentence embodies pretty well this :
Plans are useless but planning is indispensable (Dwight Eisenhower)
Specifically, when technologies are involved, the plan appears critical as investments are required for a long period and some technologies may be the foundation of competitive advantage.
The attractivity/difficulty matrix and prioritisation options
One very good way of clustering opportunities for selecting which to start with is to use a 2 dimensions matrix: attractivity vs difficulty.
Attractivity refers to the potential impact of each opportunity and the estimated financial impact may be used to rank the opportunities. On the other side, difficulty refers to the efforts required to implement each opportunity. Set-up costs and lead time can be used to rank the opportunities.
As you know if you’ve been through that kind of work, the assessment of both attractivity and difficulty dimensions may be subject to a lot of debates and arguments. In particular when it’s the first time such initiatives are implemented.
Once done, you have 4 clusters
- Gold mine: Attractive and easy to implement
- Moon shots: Attractive and difficult to implement
- Quick wins: Less attractive and easy to implement
- Questionable: Less attractive and difficult to implement
Several planning options may then be considered according to the specific internal situation of the company and the competitor’s positions. When the short-term competition pressure is higher or when the current performance is on the downside, a mix between Quick wins and Gold Mine may turn to be a good start. If the company is under less pressure, starting with Moon shots may be useful to pave the way for a sustainable advantage.
Adding two other dimensions to prioritize
But this way of clustering the opportunities doesn’t account for two other very important dimensions to consider when prioritizing them: which opportunities share the type of the same resource (leverage) and which opportunities are leveraging the company’s distinctive capabilities (strategic fit).
Leverage. The resources required to implement an opportunity may be of several types: human, technical, or cultural. If the marginal cost of an opportunity is lower when launched at the same time as more attractive opportunities because they are using the same resources, then it would make sense to launch them at the same time to benefit from a higher scale effect.
Strategic fit. Data and analytics resources deliver more value when they apply to the distinctive capabilities of the company. For example, leaders in manufacturing leverage much more than service-based companies’ automation and optimization opportunities because they already built strong capabilities in both domains. The more an opportunity applies to an existing strong capability, the higher the priority would be, because the chance of delivering value creation is higher.
Leaving some room for flexibility
This evaluation of opportunities is dynamic. As time goes by, some opportunities may become easier to implement. For example, because the company grew its asset base or because a new technology emerged. Similarly, some opportunities may become more attractive over time. The evolution of clients’ habits or performance criteria may have an impact on the attractivity of some opportunities.
This is why the benefit of this categorization and assessment is more to foster a shared vision within the company on the situation than a fixed plan to implement. In addition, such effort make easier the interpretation of market evolutions and the capture of new opportunities. For example, acquisition or partnership decisions are made easier when this analysis has been done.
Photo by Justin Campbell on Unsplash
