5 steps board should take to make the most of customer data

Thursday, 27 September 2018


    Boards need to be alert to the silent voice of the customer by assessing data, testing and validating assumptions, writes NRMA Chair Tim Trumper MAICD.

    A lasting impression left by the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry was the failure to put the customer at the heart of the organisation, and to better understand and focus on their needs. A culture that puts customers first is critical for long-term success in a competitive market. Business has always been about what your customers want and how to give it to them before your competitor. Now the art of finding out what customers want is the frontier of technology, analytics and artificial intelligence (AI).

    “Governance” comes from the Greek word meaning “to steer”. Good captains are always looking for better, faster, more reliable data to steer any ship, plane or enterprise. It’s timely for directors to ask if their organisations are making the best use of all available data to steer the business towards the customer-driven future. It is the organisational cultural challenge of our time — to structure properly to capture customer clues, market nuances and ensure you’re asking the right questions.

    The Economist (May 2017) claimed: “The world’s most valuable resource is no longer oil, but data”. A decade ago, the world’s largest companies were (mostly) oil, automotive and big pharma. Today, they are technology companies — Google, Apple, Amazon, Alibaba and the like — and all are data-driven and customer-centric by design. These companies know that data is the contemporary “voice” of the customer and to miss the clues for innovation in this voice will be serious.

    Whatever business you’re in, your customers will also be customers of some or all of these tech titans. They will know what the one-touch-buy option on Amazon looks and feels like, and be using its recommendation engine. The local shoe shop’s customer service will be compared to Amazon’s because all our customers overlap.

    Over the past decade, high-value customer signals have moved from small, noisy data sets such as focus groups, surveys and Twitter feeds, to large-scale customer data sets — applied analytics, self-learning algorithms and, increasingly, AI.

    Directors now have a responsibility to ensure their organisations are both competitive and ethical in using data to improve their understanding of their customers. Large-scale data and AI usage is frequently the differentiator between thriving companies and those that are not making the grade.

    There is a direct link between superior data use and business performance. One example is the Australian vitamin company that used a combination of de-identified data sets showing that some of its largest sales in Australia were then being exported by individual entrepreneurs to China. The board’s thinking was, “If our customers can make a business out of arbitraging our product to export, why aren’t we doubling down on our Chinese exports right now?”

    Subsequent actions drove large-scale increases in investment and returns for shareholders. The customer signals became part of the strategy because the company had the systems to see and use the customer clues.

    Data challenge

    A review of board papers will reveal a gap in hard data on customers and real insight into current and future customer needs. Most organisations face a challenge to capture and curate data into potent actionable signals for the deployment of capital or other strategic functions. Initial innovations like Net Promoter Score (NPS) — an index measure of the willingness of customers to recommend a company’s products or services to promoters, passives and detractors — have now become the norm.

    Best practice sees businesses measuring billions of data points on customers and turning them into automated predictive actions to benefit customers. One US video-streaming service has the intelligence to detect buffering or poor user experience and issue an immediate credit — often before the customer has even noticed the problem. Such initiatives are redefining how consumers view good service.

    As customers, we judge businesses by those that set the bar high. For example, Netflix’s data-driven content-recommendation engine drives 75 per cent of viewer use. (Next time you go online to pay an energy or telco bill, compare the functionality to Netflix.)

    The algorithm that keeps customers coming back to Netflix is a core asset. Netflix has created the “entertainment DNA” of each customer’s preference for content, which is clearly serving customers — and Netflix — well.

    Companies whose boards are not asking how to create such innovation tend not to be performing on the same trajectory as Netflix. It is arguable that a company’s lack of capability to create customer-driven recommendations should be added to the organisation’s risk register. Data is now too important to be parked with the tech team or data department, and directors have never had more potent and actionable data to leverage. Successful innovation from privacy-compliant customer signals and insights is now a board conversation — which is a governance issue on any measure.

    Alibaba does not have a data business — it has a customer business that runs on customer data and signals. Automated data-centric systems provide similar scaled customer service and solutions for larger businesses.

    The small country general store has always known which customers like the wholemeal bread and which ones preferred the plain white sliced bread — knowing it’s simply good business to deliver good service.

    Woolworths in Australia uses a system built by Quantium, which makes nearly five million decisions a second, creating an individual recommendation for each customer. No two customer recommendations are the same, as the algorithms are highly complex and focused on making the recommendation for each customer’s benefit. Such innovation allows organisations to enhance the customer experience while building more confidence in other areas that data and automated decision engines can be used.

    What the board should do

    1. Put customer data on the agenda: Formally discuss how your company uses and is placed to win in a world of data, analytics and AI. Test existing assumptions about customer likes and dislikes with data that validates or challenges these assumptions to ensure they’re based on fact, not opinion. Get inspired by external advice on what is possible with data. Review case studies from around the globe.
    2. Assess skill levels and cultural barriers to data-driven decision-making: Which skills are needed? Which partners could help?
    3. Benchmark how you use customer data: How does your website functionality stack up with your local competitors and global tech companies? (At NRMA, we recently re-engineered the customer renewal process to become a one-click process — a change that resonates with customers.)
    4. Seek expertise: Boards take advice on legal, financial, strategic and other matters. Data use is relatively new and complex, so find the expertise to get there faster. Advice on data use should come from a company with a proficiency in data use and governance that has repeatedly achieved commercial success for its clients. This is not a software or hardware conversation — it’s about strategy, customers, competitive advantage, profits and innovation. It will be unlikely that your organisation can do this alone. Use your network to talk with other directors about external advice they have found helpful.
    5. Test and learn: Try zero-cost options such as Google Analytics to gather data from your website. Check out new data-driven ideas, monitor them and build organisational confidence. New techniques exist for running A/B testing of websites and they can be installed at low cost. Try controlled tests to learn more about customers. (One tech company even tested which side of the kitchen its staff preferred the fruit bowl, measuring consumption at weekly intervals.) Building a test-and-learn culture is a big part of the data frontier.

    Directors of companies not founded on new technology have a challenge to create a culture and operating environment where customer signals can be identified, analysed and integrated into automated decision-making processes. Retro-fitting this capability into a traditional business is the governance challenge of our times. It is what a digital transformation is at its heart. Boards that are good at learning this will see exponential returns.

    As [former GE CEO] Jack Welch said, “Our ability to act on that learning faster than others is the ultimate competitive advantage” — and that learning will be from ethical and strategic use of data.

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