Do you understand the true significance of Big Data in the Food Industry? Knowing your target audience is critical for any type of business. In this article, we will look at the best practices and data-driven strategies (not just marketing) for the food industry. Food and beverage is a large and complex industry with many different players, some of whom are interconnected. The ecosystem includes small producers as well as large multinational brands, players who cater to everyone as well as those who target a specific niche, distributors, clubs, small and large restaurants, and retail chains.
Can we find a common denominator that applies to all aspects of this industry? Yes, the answer is yes. It is this: all aspects of the Food and Beverage Industry have a significant impact on people’s lives. As a result, they are extremely delicate and subject to a high rate of variation. They are also unpredictable.
After all, this is something that affects almost every industry, but in the food industry, the impact of the individual is particularly significant. Translated: For brands, restaurants, store managers, and marketers in general, understanding the audience in the most precise, in-depth, and functional way is critical. Knowing one’s target audience is one of the oldest commercial and advertising mechanisms. In this case, however, we’re dealing with a potentially massive customer base.
How can such an audience be tracked, how can one “know it”? Is it possible? Yes. Brands can gain an in-depth understanding of their audience thanks to digital and the use of cutting-edge tools. In particular, we are discussing the analysis of Big Data, or the digital traces that we all leave online. Starting with this analysis, we must develop a data-driven marketing strategy.
There is no one-size-fits-all strategy or set of tools. It is a matter of applying these technologies in an intelligent, precise, and, most importantly, functional manner to one’s processes and goals. The goal here is to address the individual in a truly tailored, one-on-one conversation.
According to a Nielsen survey, 63 percent of US marketers believe that data-driven marketing is one of the most important and decisive marketing tools available. And one thing is certain: this centrality will only grow in the future. But, when it comes to the world of Food and Beverage, what are the most effective strategies and best practices?
Big Data’s Importance in the Food Industry: 5 Strategies
We’ll take a look at five of them below. We’ll start with a quick look at the production front (i.e. optimization and cost reduction) and work our way up to the frontier of true personalization, all the way to loyalty. As we go along, you’ll notice that all of these elements are interconnected.
The starting point: cost reduction and optimization
Before we get into marketing, let’s start at the beginning: production optimization and cost reduction.
In this field, data analysis is extremely valuable. We’re not just talking about historical data that can help us identify process inefficiencies and areas for improvement (also thanks to technologies like the Internet of Things, a potentially endless new source of information). The data analysis also assists us in being able to “predict the future” to some extent; we are already in a territory halfway between production and marketing.
Let’s get right to the point with a Danish example. The Salling Group (formerly Dansk Supermarked Group) is the country’s largest retailer and a pioneer in the predictive use of data. Its constantly updated analysis systems allow it to track the preferences of customers who enter its stores in near real-time. Based on this, the company can determine the purchase and storage volumes that are required. It is worth noting that this is a business that deals with a massive variety of products, over 1.4 million customers per day, and a significant volume of fresh products (which requires a certain delicacy in managing the freshness of its stock).
The benefits of a data-driven approach and how it can result in cost savings, increased user satisfaction, and waste reduction are obvious. But, more importantly, the company (and its marketing departments) use this massive amount of valuable data to make strategic decisions with a level of awareness and potential for success previously unimaginable.
Sentiment Analysis and Prediction
The sentiment is the index that measures what is “said” about a brand or a product, whether positive or negative. Depending on the context, this can be generic or very specific in the case of a product. For example, you could look at what is being said about the topic of “red wine” in a global context, what is being said about a specific vintage in a specific geographical location, or what is being said about a specific brand of red wine.
It is critical to learn how to analyze this to better understand market trends and make informed decisions. In a nutshell, data analysis enables you to offer products and services to your customers before they realize they are exactly what they want. And it enables you to do so by narrowing your focus on increasingly specific geographical or demographic segments based on previous preferences and a set of metrics that can (and must) be combined.
Choosing the Right Combination
Finding the right mix in data analysis entails first focusing on and combining the appropriate metrics.
However, getting the right data, in the right places, and at the right times requires an omnichannel perspective: mobile, desktop, and tablet; as well as knowing how to track the best keywords on search engines, social networks, and specialized platforms.
One thing to keep in mind is that there is no single perfect mix that applies to everyone; it’s all about finding the right one, the most functional one, for your business and your needs.
Considering personalization? You certainly can.
The true goal of a data-driven approach is to learn as much as possible about your target audience of customers, both potential and actual. The following step is to divide this audience into many segments based on characteristics that you define. These specific segments of the audience will be intercepted with messages tailored to them, delivered in the right “voice,” at the right place, and at the right time.
Even in the Food and Beverage sector, big brands are realizing the power of personalization. McDonald’s, for example, has launched an app through which you can not only access discounts and personalized offers but also “talk” with the brand to improve the service. Then there’s Kroger, the retailing behemoth with over 120 billion in sales and nearly half a million employees. In 2017, the company implemented an efficient data collection system, which allowed them to generate over 6 million unique and personalized offers to customers (mega-conference.com).
Loyalty: the real goal
We began this post by discussing the use of Big Data for production optimization and cost reduction (but also, at the same time, for a better knowledge of customer behavior). We then moved on to marketing dynamics and, finally, the frontier of personalization. So, why did we choose to end with the word “loyalty”?
Loyalty is the true (and most difficult) goal that all businesses must strive for, and those in the Food and Beverage industry are no exception.
According to an in-depth analysis conducted by Bain & Company, acquiring a new customer costs 6 to 7 times more than retaining an existing one. In short, the numbers themselves “scream” the significance of loyalty. Getting to know the customer’s characteristics, behaviors, and needs is, without a doubt, the best way to build customer loyalty.
At this point, it is hardly surprising that, according to a 2017 Gartner survey, as many as 81 percent of marketers across all industries expect Customer Experience to be the primary focus of marketing challenges over the next three years. And, in today’s world, there is no such thing as a satisfying Customer Experience without personalization.