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Consumer Goods Companies: How to Accelerate Your Data-Driven Transformation?

Many consumer goods companies are investing heavily to become more data-driven. It is not an easy journey, however, there are practical actions to accelerate it.

Consumer goods companies, in the last 5 years, have made significant investment in their time, budget and human resources to become more data-driven. Today, companies have multiple data and analytics initiatives running in parallel. There are some great achievements but many of those initiatives are still struggling and have not yet generated the value they aim for.

There are 5 practical actions that consumer goods companies can take to accelerate their data-driven transformation.


1. Avoid cherry picking; follow a structured use-case prioritization approach

There are more than 50 data analytics use-cases that a consumer goods company can apply across its value chain, and it is not easy to decide where to start and how to move on. It is critical to follow a structured approach to prioritize the use cases considering business value, data readiness, technology readiness and organization readiness. But there is one more key element to consider; the connection between the use-cases. Companies should avoid selection of the use-cases that are completely independent from each other and build a use-case roadmap considering the connection between the use-cases.

The impact of a use-case could possibly increase 2-4 times when connected with other use-cases. For instance, the value from inventory optimization analytics would increase 2-3 times with the input from well-functioning demand forecasting analytics.


2. Approach data partnerships with a change management mindset

Consumer goods companies typically do not have direct access to the critical data such as consumer information, transaction details and competitors’ actions. Therefore, they are dependent on third parties (retailers, e-commerce players, media platforms, media agents, market research companies, etc). Gathering data from this fragmented partner ecosystem is a challenge. Collaboration with the third parties needs to be driven with partnership mindset and this requires change management at two levels:

Change management in your own organization: Business teams in the organizations are working with the third parties day-to-day to run their operations and they are the right contact to drive the data partnerships. However, often it is not a priority in their list as it is hard to create an immediate business impact through data partnerships. Therefore, it is utmost important to create an awareness at the business teams about the importance of data and prepare them for the potential challenges they will face during this process. Building an effective data partnership will definitely require persistence and patience of the consumer goods companies’ business teams.

Change management in your partners: Partners of the consumer goods companies seek for opportunities to monetize their data. Some partners are more focused on direct value generation through selling data, however, there is much more value that can be created with joint-value generation efforts. Consumer goods companies can attract their partners with demonstrating and proving the value potential with focusing on the high impact use-cases such as promo optimization, personalization and replenishment optimization. 1-2 successful use-cases will increase the awareness about the opportunity size and can trigger the change in your partners.


3. Start with the data you have without waiting for more

Data availability is a fundamental challenge for many consumer goods companies. Many companies spend a lot of time and effort to reach the ideal data to push their data and analytics initiatives.

However, there is a lot that can be achieved with the available data within the companies. It may not be good enough to get the best result, but companies can easily start with the simpler actions such as creating transparency and getting more insights about their current actions, and then over-time they can further improve the analytics capabilities and get more benefits through prediction and automated actions.


4. Be open to try new approaches and take risks

There are certain challenges along the data-driven transformation; poor data quality, lack of historical data, inaccessible data etc. In order to overcome these challenges, leading companies invest more in semi-supervised and unsupervised learning driven analytics solutions where the results are not guaranteed but there is high potential to generate more deep insights.

However, it is not easy to convince the business teams to work with non-guaranteed solutions. Leadership trust and commitment to data-driven transformation is key. Leaders need to continuously encourage their business teams to utilize the analytics results even if the results are not the best quality. Data-oriented leaders know that the companies which work with more uncertainty and take risks in data analytics will move faster than their competitors.


5. Go beyond joint business planning and apply “joint data-driven execution”

Consumer goods companies have progressively increased their collobration with retailers/e-retailers over the last decades. Today, many large consumer goods companies make joint-business planning with their retail/e-retail partners. Though, data and analytics bring opportunities for new level of collaboration opportunity.

Data and analytics enable value maximization for consumer goods companies and retailers through better insights and better execution. It is not only about planning anymore, it is all about data-driven execution. In this data-driven era, retailers are responsible from providing insights about opportunity space and enabling the perfect execution of the new ideas, while consumer goods companies are responsible from bringing the insights about the value-drivers (promotions, pricing, assortment) and suggesting new ideas to improve the performance. Data and analytics are critical at every stage of this collaboration and can create significant value for both parties.

However, building this level of collaboration is not easy; it requies trust and commitment of each party, establishment of as a single team from consumer goods companies and retailer including team, authorization of team members from both parties and alignment on clear objectives and use-cases to focus on.

Overall, despite all the progress that the consumer goods companies have made, the value generated so far is just the tip of the iceberg. There are immense opportunities with following a structured use-case prioritization approach, focusing on data partnerships and being more practical and taking risks to advance in use-case implementation.