Big Data Principles Help Brands Understand Market Position

What brands should look at to make strategic decisions and plan for the future

by Frances Zelazny

A recent article in The Guardian discussed how big data can track the COVID economic slump; similar to what this shows of how economists are looking at new economic markers thanks to big data, there are signals that brands should be looking at to make strategic decisions for long-term planning.

Some of the signals may seem obvious but, for many brands, figuring out what is important and actionable is not so simple. Take, for example, the recent social unrest and the newly energized Black Lives Matter movement. In the weeks following George Floyd’s death, which served as the impetus for nationwide protests, some brands were quick to speak out in support of the movement, many fearing repercussions if they did not do so. Many brands took steps to confront racism within their companies and committed to higher employee diversity, while others made donations in support of BLM. Some companies went so far as to change their branding due to racist undertones in the name, logo, mascot or history of the brand. But many questions abound:

  • What sort of brand activities as a response to BLM have been well received by consumers?
  • What proportion of people would boycott a brand that didn’t support BLM?
  • What proportion of people would boycott a brand that had racist origins?
  • What would it take for a brand to leave behind its racist origins for good?
  • What was the consumer reaction to a particular brand’s announcement of intended activity?
  • How should brands tailor rebranding announcements to avoid alienating consumers?
  • How do brands support the movement without overshadowing their overall brand messaging?

By analyzing different market signals and obtaining good situational awareness, it’s possible to find the answers to these and broader strategic business questions like uncovering new product areas to invest in, prioritizing portfolio development, incorporating emerging ingredients, repositioning a marketing campaign, promoting a marketing message to a specific audience, tailoring an offering and a marketing strategy to a particular online channel, surfacing white space opportunities, assessing partnering, licensing and M&A opportunities and more.

Well-developed situational awareness means knowledge of what has happened in the past, what is currently happening and what potentially might happen. It’s a term with roots in the military, where it is linked to making effective decisions in the tactical environment. Winning in combat involves observing an adversary’s current moves, predicting what will happen next and being able to react before it occurs. Situational awareness is also a necessity in business; the more data that comes in, and the more connected and contextual that data is, the better situational awareness the business leader will have.

Data is more readily available than ever, but it comes from disparate sources and in various formats; more than 80% of it is unstructured and therefore, up until now, mostly unused. Going deep into product reviews, patent filings, sales data, social listening, key opinion leaders, blogs, forums and more can give brands the insight they need to plan for the future. Connecting these different sources makes it possible to then see the big picture and have a single source of truth that is timely and actionable.

Advances in artificial intelligence and in natural language processing (NLP) make this possible in a way that businesses can easily implement. Today’s configurable data platforms can automatically collect all the data and prepare it for analysis, identifying and removing incomplete, inaccurate or irrelevant components and parsing out key structural elements while extracting meaning and context (i.e., specific benefit or feature, ingredient or sentiment). The accuracy of the output and the reliability of the insights and predictions that will be gleaned from the process, however, is highly dependent on the sophistication of the NLP and the requisite taxonomies and models that are used.

For those who are able to successfully implement advanced analytics, the pay-off is huge. According to a recent Deloitte study, organizations with the highest propensity to leveraging data-driven insights in their decision-making processes were twice as likely to significantly exceed their business goals. Other research shows that companies that do implement analytics successfully are 23 times more likely to acquire new customers and 19 times more likely to be profitable.

How One Company Tripled Product Outcomes

Nomad Foods is one company that figured it out and, as a result, was able to triple product success outcomes and drive sustainable growth, even with product lines that were previously faltering and even through the current pandemic. Originally, like most of its peers, the company leveraged traditional market research and social listening to inform its product decisions. Many of the insights it ended up with were limited to a single domain (e.g., observations of consumer behaviors); dependent on analysts’ opinions, rather than objective data sets; and/or were identified too late in the trend cycle to be actionable and valuable. Predictive analytics were more focused on media spend as opposed to sales forecasting.

However, once the company made the switch to advanced analytics that tapped into external data, it was able to identify relevant macro trends in their infancy and tie them to what was happening with its product portfolio. This then led the company to new ways of managing the product development lifecycle and uncovering new products to bring to market.

Advanced analytics has, essentially, removed much of the guesswork around innovation by helping the company identify where to focus its investments and efforts. The depth and breadth of insights has resulted in a number of demonstrable business wins, notably a three-fold increase in new product launches that remain on store shelves for more than two years, no small feat in a market where 95% of new products fail.

More recently, Nomad successfully launched a new line of frozen pea-protein-based meat alternatives based on white space opportunities identified through the Signals Analytics platform. By looking at the volume of consumer discussions on the topic as compared to product availability on the market, it became clear that this was an unmet need that the company was well-positioned to capitalize on. By further examining sentiment analysis, brand managers were also able to shape product messaging around specific claims that would resonate with target consumers.

‘Business as Usual’ Planning Will Not Work Now

Since the start of the pandemic, brand manufacturers have been trying to react and evolve, and there are more changes on the horizon. From understanding how consumer needs are shifting, to what the impact of the recession is on spending patterns and wallet share, to uncovering new opportunities for the future, what has become clear for most leaders is that business as usual in planning for 2021 and beyond will simply not work. An effective approach will rely on three pillars: data, effective AI and analytics, and focusing on key business questions. More data and connected data are needed so that decision-makers can de-risk their future and feel more confident about the decisions they need to make today. But at the end of the day, proper planning begins with a specific goal or business question in mind. Once that is in place, it is possible to start beginning to look into the future.

Frances Zelazny is a seasoned marketing strategist and business development professional with more than 20 years of experience in successfully building and scaling startup technology companies. Zelazny is currently the CMO of Signals Analytics, where she drives the company’s transformational positioning as a category leader in the advanced analytics market, contributing to its aggressive growth. Zelazny has a bachelor’s degree in political science from Hofstra University and a master’s degree in international affairs from New York University.

With next-generation advanced analytics, Signals Analytics powers the future of market intelligence with the scale and speed of AI. The configurable data platform connects and classifies countless rich, external sources into unified contextual data, and augments analytic applications with unparalleled accuracy and scale to surface granular trends and predictive insights.

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