Imagine as a retail business owner you are being able to read the mind of your customers, you can decipher what is that factor that is drawing your customers’ to your product. The good news is that it is no more imaginary, nor is it the future. It is happening now. The retail business owners today not only understand the Who, What, When, and Where but also the Why. Decoding the Why enables brands to gauge why customers behaved in a particular manner. Once they unlock the Why, they have a better customer insight which is a step towards enhanced consumer experience.
In reality brands have access to a gold mine of data but they are using only 12% of it for insights. (Source: Forrester). But it is important to use data and insights at an enterprise level to materialize the customer-centric culture. This is where Customer Intelligence helps brands to first build a customer persona.
Intelligent customer analytics allows brands to segment customers for an improved retail experience. Personalized content, target messaging alongside an emotional connect with the consumers is a pathway to customer loyalty because customer loyalty is hard to achieve in a hyper-competitive retail world.
The need of the hour for the retail industry is to craft a gripping shopping eco-system whereby they meaningfully engage with customers throughout the shopping journey. The question that begs is how the retail industry shall achieve the goal.
Personalization has a crucial role when it comes to customer satisfaction. When customers end up getting personalized recommendations from a brand, they are likely to engage more with the brand. More engagement means more accurate data.
1-800-Flowers.com used AI-powered chatbots to offer improved shopping experience to its customers. They introduced a Facebook chatbot messenger for seamless ordering of floral arrangement. The function of the bot was to pick up conversational cues to recommend floral arrangements. For instance, if a customer requires something really fast, the bot can quickly come up with a tailor-made floral arrangement. The bot can also show pictures of each arrangement so that shoppers are able to choose with ease. The result is that customer feels valued with personalized recommendation and the chances to engage with the brand on several other occasions is higher.
Customer segmentation on the right metrics has a decisive role to play for enriched CX. The reason is when customers are categorized on dynamics like loyalty, frequency, and spending capacity brands, are able to deliver an intelligent personalization.
For instance, Sephora, a beauty brand based out of Paris is creating a digital disruption in the retail market. The brand offers a popular loyalty program, Beauty Insider. The program rewards user for every dollar spent on Sephora products. The accumulated points can then be redeemed to buy sample-size products.
Taking cue from this program, Sephora has started segmenting customers who are part of the Beauty Insider program. This way they are able to find out their most loyal customers and have also realized that 20% of their customers comprise the loyal ones. They are also taking the help of social media to recognize each of their loyal customers. The primary objective is to offer each of their loyal customers with ultra-personalized recommendations, predict demand, and last but not the least, meet their expectations.
With the help of Artificial Intelligence brands like Sephora are able to optimize campaigns, send the apt content at the right tempo across all touchpoints. To be precise, AI solution is like the marketing partner that helps deliver the right message at the right time.
Inventory Management: Did you know Walmart keeps a tab on the weather to decide what food items shall have a better sale? For instance when the weather is warm and dry or cloudy and windy they tend to stock more steaks while when the weather is hotter and comparatively lesser windy burgers seem to sell better. Similarly high temperatures with light breeze bring in more demand for salads; and berries do well on a clear and sunny day. This is precisely, effective inventory management which means to have the right amount of the right product at the right location for an improved CX.
This instance of Walmart is the use of artificial intelligence services for inventory management. AI enables brands to obtain Customer Intelligence services by optimizing algorithms. This in turn helps them to keep up with the demand and supply, reduce instances of stock depletions, and maximize stock levels, timely replenishment, and certainly higher ROI. The area where CI harmoniously blends with AI is in the analysis of customer behaviour and accordingly stock up or down the inventory.
Dynamic pricing is an art of being able to price your products low enough so that your customers feel valued while making as much money as possible. With the infusion of Artificial Intelligence and Machine Learning in ecommerce, the Dynamic Pricing strategy has become more accurate. Dynamic Pricing use data to price items. Hence, the more the data that can be analysed, the price optimization becomes more effective. With AI, retailers are able to modify prices based on the current users on the website and also their behavioural pattern. To be precise, AI determines the various patterns, and Customer Intelligence is used to facilitate the price determination process taking into consideration the data that has been gathered.
Amazon’s use of Dynamic Pricing is one of the prominent examples of using the strategy in retail. It is reported that Amazon changes its prices every ten minutes by constantly scanning their competitor’s price and also the price levels that has been set by the third-party. In fact, Amazon uses the price battle between the third-party sellers and vendors on Amazon to ensure that Amazon.com always offers the best price.
Contextual personalized recommendation along with enhanced UX design is taking a completely new direction with Artificial Business Intelligence. Let us understand how this becomes possible.
Every click on the internet is an addition of data for the retailers. In fact, just with a single mouse click people are making innumerable purchases every day. Retailers today keep a tab on all these click data but the challenge is to make sense of all these data. Herein, Machine Learning comes into the picture. ML learns from every click the customer made, each review the customer read, the purchases made, dwell time of a customer on a particular page, and the past activities. With these analysis retailers create a customer persona, clubs customers with similar interests and show them relevant recommendations. Just the way, you see product recommendations when you try to make a purchase from Amazon. The result is enhanced customer engagement. This is an instance wherein Customer Intelligence becomes more meaningful in harmony with ML.
Similarly, User Experience or UX has an important role to play in boosting the sales of a brand. Retail intelligence solutions, help brands understand which areas of the web page were clicked more and which were not and consequently Customer Intelligence can be leveraged using Machine Learning to decode what caused such behaviour. With this deep learning, enhanced UX of the web page is no more a herculean task. Customers will be able to enjoy personalized messages solely based on their preferences and past activities. The end result is customers will have a better user experience and brands will leverage more sales and high engagement rates.
A use case of blending ML and UX is Google’s personalized search feature for Search Engine results page. ML collects data about links on which a user clicks on the search results page. Taking cue from that ML comes up with results related to links that a user has clicked on the past. For example, a user is searching for a trip to Brazil and one of the destinations is Amazon rain forest. The next time the user types, “Amazon” on the search bar, it is more likely to get results related to trips to the Amazon rain forest, whereas if a different user types in, “Amazon” chances are high that the search result will be directed to the e-commerce website. This is an instance, where ML has learnt about a particular user and predicts the result that is more likely to be seen.
In addition ML can go a long way in extracting meaningful insights from customer reviews. With the reviews extracted, ML leverages CI to analyse the sentiment of the customers and look for enhanced consumer experience by focussing on products and UX that will have a greater positive impact which in turn will accelerate sales.
Alongside Personalization, contextual recommendation, inventory management, and dynamic pricing, there are other areas as well wherein retailers resort to AI to obtain Customer Intelligence. With so much advancement that is disrupting the retail industry, the area that triggers curiosity is the state of digitization in retail in the years to come.
The next era of Retail
The future era of retail seems to be more digitized, but will feel more human opines experts like David Roth and Jon Bird. To be precise, technology will certainly be more powerful but less visible, and from consumer’s perspective the retail experience will be seamless and effortless – something almost synonymous to magic.
Latest posts by Shraddha Banerjee (see all)
- Link RPA with your legacy systems and enhance system integration - February 5, 2019
- Exploring the Digital Banking Landscape in Egypt - January 28, 2019
- RPA & AI – The catalyst for digital transformation in the Retail sector - January 24, 2019