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  1. Battleground 2018: AI and advanced analytics

Battleground 2018: AI and advanced analytics

What changes can one expect across as retail, CPG and e-commerce segments adopt newer business models and AI gains ground?

Published: February 6, 2018 1:43 AM
As datavolumes growand processing power improves, AI, analyticsand machine learning will become increasingly applicable inawider range of retailareas such as optimising business processes, driving personalised messaging and recommendations.

Imran Saeed

It is a whole new game in the consumer-packaged goods (CPG) and retail industry. The combination of digital technologies, new competitors with smarter business models and changing consumer behaviour is disrupting, disaggregating and dislocating the industry which once operated on a simple business model.

The dynamics of the CPG industry are a far cry from the good old days, when consumer behaviour was reasonably predictable and the retail environment was competitive but orderly. Consumers are no longer online or offline. They integrate multiple channels all along the purchasing pathway, and they do so in new and different ways as their specific needs and real-time circumstances dictate. They are also actively using digital sales channels as a primary information source for price comparisons. Retailers or CPG companies must develop a much deeper understanding of both current and emerging business models, anticipating newer options.

An IDC report mentions that artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandising, and marketing and commerce because it provides better insights to optimise retail execution. It is being predicted that 30% of major retailers will adopt an omnichannel commerce platform that integrates data analytics, cognitive computing and AI capabilities that centrally orchestrate omnichannel operating and monetisation models.

Transformation has begun

Early adopters like Amazon, Walmart and eBay are realising significant value from their initiatives — double-digit improvement in margins, same-store and e-commerce revenue, inventory positions and sell-through and core marketing metrics. A huge opportunity awaits in the following areas:

Micro-segmentation for better decision-making: To enhance customer experience, segmentation is the key. AI and related technologies are able to tap and process the data scattered over various digital and offline channels about every customer which can help retailers to make accurate customers profiles by reading and listening to data trails, understand and learn from it, and accurately recommending the best product for each target group. AI-based segmentation will provide critical on-time insights for decisions on pricing, promotions, inventory management and product assortment. AI enables scale, automation and unprecedented precision for driving customer experience innovation, hyper-micro customer-segmentation and contextual interaction.

Trade promotion management and optimisation: AI and analytics can provide promotion-related insights and guidance to channel managers, category/brand managers and financial teams to help allocate their multi-million trade fund dollars wisely and save on margin leakage. Retailers can enhance their ability to benchmark scheme performances, and extend informed and encouraging offers for maximum RoI.

Personalisation: Retailers in the US are already capitalising on large volumes of transaction-based and behavioural data from their customers. As data volumes grow and processing power improves, AI, analytics and machine learning become increasingly applicable in a wider range of retail areas. It can help then to further optimise business processes, drive more impactful personalised messaging and contextual products or service recommendations by leveraging data about each unique customer. Better allocation of media ad spends: Advertisements can be personalised based on in-store camera detections and online ad testing for gauging customer responses. Hyper-personalisation and target advertising is one of the strongest use cases for AI and machine learning today. AI models can use data sets to predict and prioritise the most successful campaigns and channels, and help place their products in front of the eyes of only the customers most likely to buy them.

Value chain optimisation: Amazon, Starbucks and Walmart have created strong incentives to put customer data to work for reducing costs throughout the entire value chain. AI and analytics presents opportunities to retailers for monitoring store traffic (in-store apps) and merchandising effectiveness at the individual store level to tailor assortments and store layouts to maximise basket size and sell through. Bricks-and- mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, optimising promotions, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders, inventory management, order processing using point of sales data. Retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years (source: McKinsey).

Dynamic pricing for increased sales: AI models and retail analytics solution can help in dynamic pricing, especially in intensely competitive segments like electronics. Retailers often use a combination of internal (supply, sales goals, margins, etc) and external factors (traffic, conversion rate, popularity of the products, etc) while designing a dynamic pricing model to enhance returns on spends. Winning in a ‘winner takes all’ digital world is becoming exponentially more difficult and it requires a long-term vision. The retail and CPG industry is up for disruption because of newer technologies, business models and digital insurgents. They are embracing e-commerce and the broader need for digital transformation throughout their businesses, recognising that the digital game requires entirely different skills, organisation, culture and approaches across all business functions and systems. But the only choice they have now is to either adopt or perish.

The author is director, analytics, Absolutdata Analytics

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