Sheila is a domain changer, having worn various hats throughout her career in different countries and industries. She crunched numbers in retail, co-founded an IT start-up, and established marketing teams, executed campaigns, and organised events as a marketer.

She previously led the global marketing team for She Loves Data, a not-for-profit organisation with the mission to empower women through data /tech education and soft skills training and co-led its brand reputation growth through and post-pandemic to become a partner of choice for businesses such as Meta, Google, Citibank, and others. Sheila currently co-leads the digital deployments in Asia Pacific in her current company in the chemical and distribution industry, in addition to co-hosting a regional marketing podcast.

Presentation Synopsis:

As technology rapidly evolves, data is also being generated at the same pace from various sources. Brand and marketing managers are increasingly pressured to deliver data-backed strategies and activities. However, the availability of data brings with it the complexity of understanding and trusting information being generated and provided.

Main Points:

  • Technology and data mapping
  • Critically analyse consumer behaviour analytics
  • Data and Brand Strategy formulation

Unlocking the Power of Data: Sheila Berman Reveals Strategies for Data-Driven Brand Success

In an ideal scenario, businesses would have their own data analysts and data scientists who would be extracting raw data, cleaning, grouping, then analysing all the data from various digital platforms. Or they would be using a Customer Data Platform (CDP) to track and unify customer engagement with the business. In reality, only companies with big budgets, usually MNCs, would have the resources to do so. For small businesses, I would suggest first integrate all the platforms that are being used and have a central repository of data. As part of the integration process, a decision must be made on data nomenclature. Finally, there must be data governance in place.

If integration is not possible, an aligned naming convention and data governance must still be in place. This would make it easier to extract raw data from various sources, remove duplicates, and analyse the information based on set goals.

The goals will assist in narrowing the focus and remove analysis-paralysis. For example, if the goal is to improve consumer engagement on campaigns, the data should be looking at the behaviour based on multiple campaigns, the results of the interaction (i.e. sales generated, products bought, unsubscribed, complaints, etc), time of day and day of the week with the highest open rate, etc`. The information can then be used to improve the next campaign.

Knowledge of cultural differences are very important when it comes to interpreting consumer behaviour analytics. Poor performing brand and marketing activities may be attributed to the wrong factors and could impact the overall business. Certain countries can have strong attachments or negative sentiments to certain colours, symbolisms, people, etc. due to historical, cultural, or religious reasons.

When analysing any kind of consumer data, a visual representation needs to be part of the analysis, because numbers only tell part of the story. Research should also be done on the ground before any type of wide-scale activities are done, and once done, to verify any hypothesis before finalizing the data analysis and proposals on brand activities.

Brand strategies work hand in hand with the wider business strategy. It cannot simply be executed on its own, nor can they be only attributed to one set of consumer behaviour analytics. A successful brand strategy implementation requires business support both at the leadership level and at the ground staff level.

One of the best examples is the rebranding of Kmart Australia and New Zealand during the time of then CEO Guy Russo. Prior to him taking the helm in 2008, the retailer was in danger of disappearing. Sales were down. Customers were confused about what the brand was about. Under his leadership, the company became the most successful retailer in Australia and New Zealand. Sales, inventory levels, and prices were analysed in addition to employee and customer feedback and behaviour.

The company was then repositioned as a retailer offering everyday low prices for the family. All the products had to be items that can be found in an average home and priced low. Discounts were no longer offered unless to clear remaining stock. The success metric was the profit that was generated (100% 2 years after the rebrand), the increase in foot traffic, number of visits to stores, items per basket bought, and the positive feedback from customers.

It will depend on the area of business. Mauritius as a country is made up of people and businesses who have varied interests. People also behave according to age group, profession, culture, and possibly even religion. Businesses must refer to their target market, be clear about the segment they are after, utilize the data generated by their own companies, then look at the data from external sources to check current sentiments and trends of the wider population.

Once companies are clear on their customer profile and their customers’ preferences and buying behaviour, they can then tailor their brand strategies to ensure they are in sync with their customer’s current and future wants and needs, as they evolve as a business.

Consumers, while hesitant to provide personal details, will still share information if they can see greater or continuous value in providing them (the discount given, the white paper received, the demo seen, the free tutorials or coaching taken, etc.). It is only when the trust is broken (i.e. getting spammed by company affiliates, unable to unsubscribe easily, product or service was below what was expected, etc.) or when the benefit is no longer seen as valuable will consumers complain or withdraw their consent.

It is the responsibility of companies to ensure data security is in place both technologically and culturally, the latter via continuous education. Everyone in the company should understand that they are all responsible for securing customer data.

At the basic level, handling consumer data is about having transparent data privacy policy, preferably on the company website, clear reasons for obtaining personal information, clear information on how the personal details will be used, and a data security policy in place communicated and understood by everyone in the company.

One trend that will continue to dominate is the use of generative AI and machine learning as the likes of Google, Microsoft, Adobe, Meta, etc. invest in them. Another is the increasing affordability of consumer data platforms or similar platforms which will allow more businesses to have a unified view of their customers.

One concerning trend that has emerged because of generative AI and will continue for the next few years is the reliance by smaller businesses on these platforms to understand consumer behaviour trends instead of extracting and understanding their own company-generated data. This will lead to generic information and sameness in branding strategies.

Savvy brand strategists should use a combination of generative AI information and zero- and first-party data to differentiate themselves via rebranding at one of the spectrum to more personalized communication, improved customer interactions on various channels, better product / service offerings, and competitive pricing on the middle of the spectrum.