Retailers have high expectations from Artificial Intelligence (AI) to run a successful marketing automation campaign. But, they are confused about how to invest correctly in these technologies.
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Retailers need to be smart about utilizing AI technologies to drive optimum results from the successful campaign. To survive the stiff competition, marketers rely on technology for data collection, management, and analysis. Meeting the lofty predictions of successful AI application is not easy, and retailers need to invest smartly keeping the key needs and benefits in minds:
Predictive analytics tools analyze data to forecast future trends, and also improve customer experience and customer service. Based on AI’s learning capacity, predictive analytics can analyze customers’ purchase behavior much more effectively than ever. As per research by McKinsey, AI adds incremental value over other analytical techniques in 87% of cases in retail.
Collection and Analyzing First-Party Consumer Data
Machine learning (ML) algorithms should be employed to identify patterns and decode essential variables to give visitors the right contextual recommendations. Retailers have lots of first-party consumer data, which often goes unused. This data is typically collected from past orders, point of sale systems, mobile apps, loyalty card data, in-store sensors, and cameras. Its humongous size can be intelligently leveraged by ML systems, decoding it to draw predictions for future marketing campaigns.
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Sorting Physical Locations
Once consumers have identified themselves as a regular or loyal, and they get enrolled for loyalty programs, it becomes easy for an AI-driven system to draw more specific conclusions based on which strategies can be made. Also, in-store behavioral data analysis can hugely guide product recommendations online. AI can optimize and reduce the divide between offline and online shopping experiences. For example, customers could be targeted with relevant product cross-sells once in-store, based on their past online purchases.
Content and Product Recommendations
Product recommendations have been the backbone of retail marketing with giants like Amazon doing it over a decade. But, it’s exciting to see AI and ML creating a new level for product recommendations. To develop accurate product recommendations, retail firms should ensure that the right training data is available. Brands can combine different types of AI with being able to automatically tag text and images for AI to group products that go well together for alternatives, upsell, and cross-sell.
Product Combinations and Visual Search
Marketing automation goes way beyond the usual logic as it can match faster and better. AI and ML applications can scan all the available products online, referring to the visuals and suggest other complementary products that have the highest chances to be bought. For example, in the case of fashion brands, such tools can help in automatically generating a “complete the look” feature on the website. Many online retailers are also using AI-based visual features for providing virtual trials on the website.
Retail marketers use predictive analytics to “reverse engineer” consumer segments, offers, actions, content, and touchpoints to determine the marketing strategies with positive results. Many top brands like FedEx also rely on AI to predict which customers will turn to competitors for the next purchase with up to 90% accuracy. These applications help to identify which segment of customers are more likely to cancel the subscription or invest in alternatives.
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Also, predictive analytics is needed to help brands anticipate the volume of customer service staff required to manage the store. Measured staffing saves money for retailers by not overstaffing while having the right number of people in hand to ensure excellent customer experience during peak times. The application of AI in marketing automation for retail has been there for years, and the majority of applications are evolutionary. AI allows marketers to continue with their usual way of operation – only making it better and faster.