Retail Reputation Management With Machine Learning

Having discussed the benefits and uses of machine learning for demand forecasting and customer analytics in retail, we decided to move on to reputation management. It is no secret that a brand’s reputation can make or break a business, especially a highly customer-centric one like retail. An issue with a product or a poor customer service experience can put a huge stain on the reputation of any retailer, and it can happen quickly. With customers always online and with an ability to instantly share their experiences and leave reviews - it is more important than ever to monitor your company’s reputation and manage it effectively.

It can be rather difficult and highly time-consuming for retailers to monitor what is being said about their brand and products. Research by Clutch revealed that 42% of companies monitor their online reputation daily. Can you imagine how many labor-hours that amounts to in the long-run? Luckily, as artificial intelligence, machine learning and natural language processing technologies have emerged, it became easier to manage a company’s reputation and make sure that a brand is always portraying the best possible image to its customers. These tools allow the most time-consuming work to be done within seconds by machines so that managers can dedicate more time to strategic decision-making.

In today’s post, we will look at two ways that machine learning technologies help retailers with reputation management - social listening and customer feedback analytics.

Social listening

Social listening allows you to monitor social channels for mentions of your company, competitors or products and gain insights on the overall state of your reputation. It can also help you see how your competitors are viewed, what products are most liked and what people are saying about your brand. These insights then allow you to learn more about your customers, their likes and preferences so that you can create better experiences for them.

In short, social listening helps you understand the “why” behind customer behavior so that you can adjust your strategy. For example, when you see a lot of positive reviews about a new product launch, social listening can show you that perhaps it's not just about the great design. Maybe what resonated with your customer base was the personal story you used in your marketing campaign. Thanks to this insight, in your next campaign, you could test more storytelling strategies to further increase your ROI.

Customer feedback analytics

One step further in reputation management are customer feedback analytics which help employees quickly understand what is being said about the company and respond accordingly. Algorithms go through comment forms, surveys, and reviews in real-time and provide immediate insights on reviews that require attention. Thus allowing to save significant time on information processing.

How does it work? It all begins with natural language processing which is a field of artificial intelligence and machine learning that analyzes the human language and derives meaning from it. The key benefit of which is - not needing a human to go through hundreds or thousands of text content manually.

Then, the process of sentiment analysis begins. It is used to determine the attitude and emotion expressed in a text and categorize it as either positive, negative or neutral. After examination, it allows for real-time insights to be provided so that employees immediately see any customer complaints that need an urgent response or just so that they can get a better understanding of how their customers feel about the company.


As you can see, machine learning and language processing make reputation management even more effective, primarily thanks to the speed of access to insights, which leads to:

  1. Better customer understanding - by monitoring what is being said about your business and gaining quick access to overall review scores, you can get closer to your customers by having a better understanding of their preferences.

  2. Improved strategic decision-making - since you better understand your customers, your strategy can be driven by true insights instead of hunches and you can be sure about creating products and experiences that your customers will appreciate.

  3. Time saved on manual monitoring - as you leverage natural language processing and machine learning algorithms - you will no longer need to have employees spend hours on monitoring mentions and social media.

  4. Increase brand loyalty - by leveraging the relevant insights you gain with reputation management tools, you will be able to quickly respond to your customers and build a thoughtful communication strategy, for which you will be thanked with a loyal customer base.

  5. Growth in revenue - as your relationship with your customers becomes better and brand loyalty increases - customers will be choosing you over the competition, thus driving sales and revenue.

Check out our latest eBook on how AI & ML help retailers boost profits!

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