Maximizing Business Outcomes with Data and Its Applications for Retail and Consumer Goods

Like many other industries, the retail and consumer goods industry measures business outcomes in the context of the customer life cycle, customer experience, and customer lifetime value. However, unlike other industries, the retail and consumer goods industry is moving from competing on products to competing on customers. In this situation, how can data help organizations to maximize their business outcomes?

The Drive to Become Data-Driven

The retail and consumer goods industry has always competed on both products and customers, with a particular focus on products. However, as technology evolved, enabling copycat products to appear quickly in the market, the focus of the retail and consumer goods industry moved to competing for customers.

This means retail and consumer goods organizations need to know and understand their customer and how their customer interacts with their product better than ever before. Unfortunately, a lot of the data needed to do this is currently out of the organization’s reach, driving them to become more customer-centric to fill this data gap.

Being so close to the end customer, the retail and consumer goods industry has always been one of the most mature sectors when it comes to gathering and using data. To maximize their business outcomes, organizations will need to start to leverage their gathered data and reach the next stage of data maturity.

But can organizations achieve this? Previously, each department in an RCG organization gathered and stored its own data and used its own applications to carry out its responsibilities. Management then integrated all data sources and applications, with standard processes and connected data sources. While this provided a better customer experience and more insights, it only allowed the organization to produce standard products. As data becomes part of the fabric of an organization, it requires a change in mindset. Organizations need to use the data to discover exactly what different customers are looking for and then tweak and differentiate their product range accordingly.

Data can also open up new business opportunities, empowering retail and consumer goods organizations to monetize their data and adapt their business model. This works by organizations creating a cross-section of their data, anonymizing it, and then selling it to other organizations in their ecosystem as a marketing service. For example, a take-out delivery company has data about the locations of customers who order take-out food, including the frequency of orders and the type of food the customers prefer. This would be useful data for an organization that is planning where to open their new take-out restaurant.

It is important to note that the data on its own is insufficient to maximize the business outcomes. Knowledge about the local markets and customers is equally important.

Gaining a B2B2C Perspective

Many organizations within the retail and consumer goods industry have realized that to stay relevant in the future, it is essential that they connect with their end-customers and create or develop stronger bonds with them. This realization is driving the industry’s transformation from a B2B focus to a B2B2C perspective.

However, even though the retail and consumer goods industry is relatively close to the end-customer, it is not always easy to develop a B2B2C perspective. Organizations need to adapt their business models and form collaborations with ecosystem or value chain partners in order to be able to access external customer data. The business value of these partnerships depends on the accessibility of the data, the insights generated, and the level of collaboration.

Retail and consumer goods organizations have moved beyond sharing transactional data such as updates, exceptions, and changes with their partners via an electronic data interface (EDI) and are now considering how to tackle data sharing, the next level of data maturity. Unfortunately, there are not yet any official standards for data sharing, which adds to the complexity of it. 

One option is to use a data cleanroom, but this method has limitations including requiring all partners to use the same technology platform, which isn’t always feasible. New concepts, like the insight marketplace are becoming available to start sharing and consuming data in the ecosystem with the use of application programming interfaces (APIs). This facilitates the leveraging of the data available in the ecosystem to create better outcomes. Combining data with applications has become easier.

Additionally, it is not straightforward to jointly create insights that are valuable for both the organization and its partner.

An alternative is for the retail and consumer goods organization to create new routes to the end-customer which bypass retailers. While this can bring benefits in the form of increased end-customer data, there are issues that need to be addressed. To give an example, retail and consumer goods organizations would need to change or duplicate their internal processes, so they are no longer focused on purely providing their goods in bulk at regular intervals to retailers. Instead, they would also have a larger volume of smaller sales to individual customers, which could cause conflicts in their internal supply chain. Also, retailers could begin to see the retail and consumer goods organization as competition as well as a supplier.

The Role of Data

Data applications, including hyper-personalization and automation, provide insights on different ways to influence customer activity and increase customer lifetime value. This is essential for retail and consumer goods organizations that plan to achieve the next level of data maturity and maximize their business outcomes. To give a few examples:

·       Data scientists that work with AI can analyze a wide variety of factors to predict future demand, reducing the organization’s environmental impact, optimizing production, and increasing efficiency.

·       Hyper-personalization focuses on providing customers with a better experience by putting them at the center, and hopefully moves customers from making occasional purchases to being brand advocates.

·       Automation is useful for analytics as it is no longer possible for humans to keep up with the volume and speed of data that organizations need to analyze in order to make informed decisions. However, there is still a role for human intervention in analytics. This depends on the sensitivity of the data, the business value, and the price point of the products in question.

·       Data can also be used to optimize pricing for a selection of delivery channels, analyzing the most profitable way to increase the organization’s Net Promotor Score (NPS), improve customer satisfaction, or identify the most profitable customer demographics. These insights can influence decision making to maximize business outcomes.

Data Sharing: Privacy, Ethics, and Legal Issues

When it comes to data sharing there are a number of privacy, ethical, and legal issues that organizations need to be aware of. Some of the most relevant currently include third-party cookies, anonymized data and data profiling.

Firstly, invisible third-party cookies are currently included on many websites which send customer’s browsing history to large tech companies and other organizations. While these are currently legal, there are indications that this could soon change.

The General Data Protection Regulation 2016/679 (GDPR) and other and other privacy regulations make it illegal to share identifiable or anonymized data without permission.

Data profiling of customers and then offering them different deals is common practice for a lot of organizations. While this is legal, is it ethical? For example, can you explain to your customer why they have been offered a product at one price while their neighbor has been offered exactly the same product at a different price?

Data and Its Applications in Practice

When it comes to implementing a data strategy, there are several points that retail and consumer goods organizations should be aware of:

Bottom-up or Top-down?

Data governance can be seen as both bottom-up and top-down. From a bottom-up perspective, employees throughout the organization need to be open to sharing data transversally. However, there needs to be a single owner of the organization’s data, ensuring that data becomes an integral part of the organization’s strategy. For this to happen, the data owner needs to be part of management, preferably in the C-suite.

Part of a Larger Value Strategy

It is also important to note that while organizations might talk about data and data applications, they very rarely decide to invest in a particular solution. Instead the selection of the right data application is part of a larger value strategy that has positively evaluated that the data gathering and analysis will create additional new value.

Data Lakes

When organizations create data lakes, they are frequently unused. This is often due to employees not knowing the data source is available or the data lake not being properly managed and leveraged. It’s not enough for organizations to provide access to multiple data sources, the data needs to be correct, connected, and available.

Looking to the Future of Data and Its Applications

As we have seen, data and the way we use it can take retail and consumer goods organizations to the next level of data maturity, maximizing business outcomes as they transform from a B2B focus to a B2B2C perspective, creating value for both shareholders and stakeholders.

Is your organization ready to benefit from data and its applications? Learn more about how AI can play an important role in staying relevant and seizing business opportunities in the future.