Season 5: Innovating for impact

Innovative thinking for finance leaders, David Barton-Grimley
Part 3 of 4

Changing the way you operate in finance

One-eyed woman discussing with an older woman

Now we can see that innovation is not just delivered through specific financial means, but through taking a step back from the operational reality of our business to see both the blind spots and opportunities available to us, we can think about how a finance function enables the process of innovation.

Of all the principles and methods which you can use to adopt an innovative mindset, experimentation and agility are the most relevant for a finance function as I covered in part 1 of this masterclass.

A quick recap of agility and experimentation

Experimentation involves systematically testing new ideas, products or processes to gather data, learn from outcomes and iterate toward better solutions. Agility in our context refers to the ability for the organization to run these experiments repetitively, and rapidly “pivot” to adapt to the new market conditions.

In practice, these two principles work together. Let’s look at a practical example of a typical experimentation process in 5 steps:

  1. Formulate hypotheses: A clothing company has a hunch that launching a new line of sustainable activewear could appeal to environmentally conscious customers and drive growth through sales. This could be based on a market signal indicating a preference for sustainable fashion, but also higher conversion rates for a guest brand the company featured last month.
  2. Design and implement an experiment: Next, an experiment is designed to test this hypothesis by introducing the new sustainable activewear line to a small set of customers. The experiment is designed to run for three months, during which sales performance, customer feedback and brand perception will be assessed.

    A small run of garments is produced and launched with a marketing campaign highlighting their sustainability features and benefits to environmentally conscious consumers.
  3. Measurement and analysis: Data is collected on sales performance, customer demographics and online reviews. Surveys and focus groups are conducted to gather qualitative insights into customer preferences, purchase motivations, and brand perception. At the end of the experiment, the clothing company analyzes the collected data to evaluate the success of the new sustainable activewear line. Sales data reveal strong demand and positive customer response, with higher-than-expected sales volumes and favourable feedback indicating a positive brand perception among target consumers.
  4. Iterative learning: Armed with insights from the experiment, the clothing company iterates on its product designs, pricing strategies and marketing messaging to further optimize the sustainable activewear line for the target market. Based on positive outcomes, the company decides to expand the rollout of the new product line to additional markets and distribution channels.

How might finance facilitate this process?

It should be clear to see just how much emphasis there is on both access to data and calculated risk to make a successful experiment, and here’s where finance truly comes in as both that source of data and enabling culture change. Let’s explore more:

  1. An enabler of clean, joined up data to product teams to drive insight generation and capture

    We’re increasingly awash with data, often stored in diverse systems. Think about customer purchasing history, demographic data, website traffic and inventory levels. Despite major advancements in software, small businesses still struggle to leverage their data. In fact, according to a survey by SCORE, 67% of small businesses cited a lack of understanding and technical skills as primary obstacles to extracting the right level of insight from their systems.

    As a rule of thumb: data has limited use without the skills to interpret it, and here’s where finance teams come in with those fundamental analytical skills. As the wider business invests in new tools which generate data, those skills are becoming increasingly important to help businesses derive insight from data.

    If you can get this right, the benefits are significant. Research by Deloitte indicates that small businesses utilizing data analytics are 2.2 times more likely to exceed revenue expectations and 1.9 times more likely to have above-average profitability.
  2. Helping the business become more agile

    Next, we need to deploy that data in the right way, and here we can see finance playing a directly active role in the innovation process. The most obvious is, of course, in measures. For example, is there an ROI? Can we forecast an increase in revenue/reduction in cost/improvement in profit margins? What return do we need to see from this experiment to continue investing? However, for much smaller businesses which can’t dedicate specific resources or teams purely to innovation, your role can power decision making.

    Business leaders tend to be very good at identifying a market opportunity, but less adept in assessing investment costs and risk/reward trade-offs. The more of a stretch that opportunity is, the more important your role becomes in both adding rigour to the opportunity available and boosting the overall productivity of innovation. For example:
  • How might we bring in more third-party data to add confidence to the opportunity available?
  • Can we find a way to boost the efficiency of the way we prototype? For example, can we run simulations or produce mock-ups before commissioning a production run?

Your indirect role as a coach and enabler is also critical. You and your team can’t be everywhere at once, and your analytical skills are of critical value. According to research by Sage, 56% of CFOs are preparing for a heightened demand to offer broader business counsel, stepping outside the traditional financial domain. It’s critical to think about how you can add value through training and culture change, for example:

How might I train product teams to think more commercially? Could you build a framework or guidelines for product teams to build their own measures, or are there a set of simple measures which you can develop and train?

Pick an opportunity you had found in your business from the first exercise and imagine how you might enable the business to address the opportunity.
Questions you might consider:

● Does your business have ready access to customer, sales and finance data? How hard is it to get this?
● What blind spots do you have in your data?
● How much of your (or your teams) time would it take today to provide this data?
● What needs to change in your software? Do you have it, just not stitched up or do you need something new?