Until recently, it was enough for retailers to simply have a presence online and in-store, with some brands also providing an app experience. But this omnichannel approach is becoming quickly outdated, with a unified experience now setting leading retailers apart.
With this approach, retailers strive to offer a linked, seamless and personalised customer experience across all channels – whether the customer is shopping from their sofa or local store. According to some brands, shoppers who engage across platforms are two to four times as valuable as those who only purchase from brick-and-mortar stores.
However, achieving unified commerce requires significant strategic and technical consideration. Artificial intelligence (AI) offers some support, helping to optimise the customer journey while potentially boosting revenue. Statistics show that over a two-year period, retailers using AI and machine learning (ML) performed better than competitors, enjoying a two-digit growth of sales and an annual profit rise of around 8%.
Let’s dive into how retailers can use AI to support unified commerce projects.
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1. Managing customer data
Collecting comprehensive, accurate and high-quality customer data is crucial to achieving unified commerce. Without it, retailers will struggle to provide the sought-after personalised experience.
AI can be used to deliver business value from large datasets. By creating unified data lakes, data from all channels can be consolidated to ensure a single source of truth. Then, ML can leverage the data using advanced algorithms to analyse patterns and provide insights into customer behaviour, preferences and demographics.
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2. Elevating experiences
With a data strategy in place, AI can build and manage customer profiles across shopping channels, mapping interactions and purchases in-store, online and on social media.
In-store technology such as smart mirrors, digital-enabled changing rooms and tech-empowered employees can use these profiles to offer customers highly personal recommendations. Online, AI-driven personalisation engines can provide product suggestions and promotions based on a shopper’s purchases, behaviours and real-time data such as weather and location.
While this relevant content is beneficial to the customer, it can also increase conversion, bring an uplift in sales and reduce marketing costs by as much as 20%. By identifying and grouping customers based on similarities in spending habits, preferences and other key attributes, AI can help segment large customer databases into smaller groups. Then, targeted marketing efforts can be made that speak more directly to the audience’s needs.
These efforts can be supported further by generative AI, which can quickly generate and distribute content based on these insights. With this type of highly personalised content creation, retailers can present customers with products and promotions within content created to directly appeal to them, based on the data gathered and analysed.
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3. Inventory across channels
For some time, retailers have dealt with in-store and online channels as separate entities with their own inventory and supply. However, with a unified approach, brands can manage these as one.
Predictive analytics and ML support retailers to forecast efficiently across all channels, predicting customer needs by considering digital shopping trends and in-store footfall. In doing so, retailers can effectively manage stock levels while reducing waste across channels.
Then, by analysing a customer’s location, brands can identify the most efficient fulfilment options from store or warehouse, or recommending the customer picks up in store. Upon decision, AI can then identify the most effective delivery routes.
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4. Unified dynamic pricing
Some customers expect a product’s price to remain constant across purchasing channels. However, this presents a challenge for retailers. Offers and promotions applied quickly and easily online can be more difficult to update in-store, where physical price tags must be changed along with in-store systems.
However, retailers can leverage AI to seamlessly manage prices across multiple systems, automating the process accordingly. This allows for dynamic pricing based on demand, expiry dates and more, while ensuring that customers can enjoy the same promotions regardless of where they shop.
Physical price tags still present an obstacle; however, things are evolving rapidly. Earlier this year, Lidl announced it would be rolling out electronic price tags across UK stores, allowing them to automate updates rather than manually changing thousands of physical price tags.
Master AI-powered unified commerce
Brands ready to move beyond an omnichannel approach will welcome the opportunity AI brings to support unification of channels.
To get started or evolve their AI strategy, retailers should:
- Identify strong business-specific use cases for AI technology
- Assess existing software, systems and data for AI compatibility
- Gather strategic and technical expertise to formulate an effective roadmap
For support, insight and recommendations to get started, download our whitepaper: Seizing the AI Opportunity in Retail. Or, for a deeper dive into unified commerce, watch our masterclass.