The game today is no longer just about products and services, but end to end customer experiences. Great experiences create loyal customers and competitive advantage. We plan strategies for customer loyalty programs or customer retention, but at the same time we forget that loyalty is a sentiment. The planned strategies can only worked if our customers have any kind of sentiment towards our offerings. If it's good, it can be bettered, if it's bad it can be remedied. But the main thing is that, what do your customers feel about you?
What is Sentiment Analysis?
Sentimental analysis is analyzing customer response or feelings and understanding human psychology. By analyzing diverse customer response and sentiments like the customer feels happy or not, when s(he) is not satisfied with company's offerings. Dealing in customer queries in this manner will guarantee more customer engagement and real time customer feedback regarding the company and its offerings. This unlocks the different opportunities to the company to make improvements and innovative solutions and leads to better customer experience. The sentimental analysis also shows the early caution signals to the company when customer is thinking to switch or it requires human interference to handle customer grievances.
Sentiment Analytics in Retail:
First thing we need to analyze that do our customers attaching their sentiments with the brand or for our product? If yes then which sentiment? The easiest way to analyze this can only be possible by gathering feedback from the customers. Sentiment analytics is based on algorithms and machine learning, so instead of word of mouth or documented information retailers have to go for digitally gathered feedback.
Thankfully, nowadays there is no lack of channels to gather data from customers. With the help of social media, there are forums, feedback forms and online surveys etc. we can easily get the feedback from the customers. Social media is the easiest way to gather customer sentiments in a cost effective manner. It is easier to implement analytics on social platforms with platforms like Facebook has its own inbuilt analytics. The metrics (likes, comments, shares, views) are also easy to interpret and we get answers faster. Which is return helps to take corrective actions to build customer loyalty.
Why sentiment analytics is important?
For small business deriving immediate feedback from the customers can be possible like by asking them whether they liked the food or ambience or would they recommend the shop to others. This is not always possible for all types of businesses. If you are small businesses like boutique or a restaurant, taking individual opinion of customers by talking to them might be easy. But then if you are a big company or an ecommerce retailer or a retail giant, maybe a mid-level merchant, it is not possible for you to ask your customers personally every day what they think about you. At the same time, it is also not very easy for each of your customers to give their opinion about you on different channels that would contribute to your image. Thanks to channels like social media, opinions spread like wildfire. Within a couple of minutes one person's opinion would harvest reactions from several others and this would eventually start a positive or negative campaign impacting the business.
In this digital era retailers need to trust on machine learning (ML) and sentiment analytics this helps them to get actionable insights from huge amount of data from various sources in a short span of time. Applying effective algorithms retailers can leverage the cognitive powers of analytics and take action to resolve issues faster.
Sentiment analytics uses natural language processing (NLP) where the algorithms are trained to find words from text that match with their database of meanings to identify positive or negative emotions. By applying same method into the practice the retailers can find out the products or services that are reaping more positive or negative responses.
After sentiment analysis what to do?
Once analyzing the sentiments of the customer's than the tricky areas should be identified by the retailers, firstly that the customer is not happy with the overall product, they are still okay with the services. This means they have some amount of loyalty for the retailer on which they can build upon. Secondly, they need to take action to rectify the negative emotion, in this case replace the product if possible with a better one.
Solutions: Taking the above example we can understand that once the problem area is defined actions can be taken to define solutions. For this scenario, the retailer can send a reply to the review that because the customer was not happy with the product, they are offering him/her easy return of the same. Or they can offer a discount on their next purchase. Addressing the issue will let the customer know that the brand cares about them and this will make them more loyal.
Conclusion: Applying analytics to customer and then comparing the sentiments with them would give retailers a clear picture of the scenario and helps them to create better strategies to build customer loyalty for the long run.