Historically, manufacturers have relied on traditional data for example, point of sale data or distributor data to measure value generation across the supply chain to achieve brand success. However, with the pervasiveness of the internet and ecommerce, manufacturers now have the potential to source additional online data including, but not limited to search data, ecommerce product listings, blogs, and social media. This can be integrated with existing traditional data to measure and draw tactical and strategic insights at each customer touchpoint.
While the onset of pandemics, like SARS and more recently Covid-19 have accelerated the growth of ecommerce, the industry has been steadily growing in popularity with Michael Aldrich first introducing electronic shopping back in 1979. Jumping ahead with Statista predicting that ecommerce will reach 22% of global retail sales by 2023, retailers who fail to incorporate this into their business models risk falling behind. Take Neiman Marcus, JCPenney, and J. Crew who in 2020 all filed for bankruptcy, while online superstore Amazon now worth more than Americas largest nine retailers combined.
While the ecommerce channel in South Africa is relatively small (1.4% of the retail industry; World-Wide Worx) compared to other more developed countries, the opportunity is enormous and a rapidly growing channel. According to an Accenture report released in August this year, SA online sales are expected to grow almost three times (19% p.a.) as fast as in-store sales (6% p.a.) in the period 2018 to 2023:
Despite South Africa’s relatively small size, over the last decade the landscape has evolved to include several pure-play ecommerce retailers and online marketplaces as well as traditional brick-and-mortar retailers who have launched online offerings (hybrid retailers). It is worth noting that while the online offering of traditional retailers is still relatively small in revenue terms, they are growing at a rapid pace. Historically, electronics and media have dominated but with the future forward shift this year, this has accelerated the rise of other categories e.g. food and personal care.
Takealot, the largest local online retailer with a 33% market share in 2019 (Euromonitor International), recorded revenue of R7bn for the year ended 31 March 2020 (Naspers Integrated Annual Report). The balance of the ecommerce market remains fragmented with Superbalist second largest (5.5% market share 2019) and Pick ‘n Pay positioned third (4.2% market share 2019) Euromonitor International).
With the complex shopper journey evolving, manufacturers need to gain insight not only at each one of the brick-and-mortar customer touchpoints, but also at each digital touchpoint.
However, data for data’s sake is unnecessary. Instead it is important to understand how to leverage off the available data to enable manufacturers to execute on their ecommerce data strategy. A good place for manufacturers to start is to answer business critical questions around placement, product, packaging, pricing, and persuasion. For example, where do products rank on searches? Are the images/description/features accompanying the product accurate? How do the price points compare across platforms and with competitors? How many ratings does a specific product have?
It is about simplifying the customer journey on the one hand, and integrating data to optimise the positioning of brands online, on the other.
While the physical touchpoints are well defined, quantified, and reported on, digital touchpoints are less understood and measured. In addition, manufacturers have less control of these digital touchpoints as they cannot dictate the search ranking, assortment, availability, and portrayal of online products.
Consumers have evolved into omnichannel shoppers requiring brands to develop a compatible data strategy that measures key performance metrics at each stage of the customer journey be they physical or digital, to succeed.
Right now, the question is not around when to invest in this strategy in this rapidly growing digital landscape, but instead, how. While it is possible to develop in-house capabilities to extract, warehouse and analyse large datasets, the barriers to success are high. A defined data strategy, sophisticated data extraction software, Big Data IT infrastructure and data analyst/science expertise are but a few requirements to develop inhouse capability.