“Opposite of love is not hate, it’s indifference.” –Elie Wiesel, Nobel laureate.
Many a time while planning strategies for customer loyalty programs or customer retention, business owners forget that loyalty is a sentiment. Your strategies will only work if customers have any kind of sentiment towards you. If it’s good, it can be bettered, if it’s bad it can be rectified. But first things first, what do your customers feel about you?
Sentiment analytics in retail:
Are your customers attaching their sentiments with your brand or product? If yes then which sentiment? The easiest way to find this out is by gathering feedback. 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 there is no dearth of channels to gather data from customers now. There is social media, there are forums, feedback forms and online surveys. Social media is the most easy to use, widely available and cost effective among the customer sentiment outlets. It is easier to implement analytics on social platforms with platforms like Facebook offering its own inbuilt analytics. The metrics (likes, comments, shares, views) are also easy to decipher and we get answers faster. Which is turn helps the retailer to take actions to build loyalty.
Why do we need sentiment analytics in retail?
Asking customers whether they liked the food or ambience or would they recommend the shop to others has been the traditional way of deriving customer sentiments. This is not always possible for all sorts of businesses. If you are small boutique or a restaurant then taking individual opinion of customers by talking to them might be easy. But then if you are an ecommerce retailer or a retail giant, maybe a midlevel 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 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 garner reactions from several others and this would eventually start a positive or negative campaign impacting the business. Since the digital era has made opinion building such a quick affair as a retailer your response time to it also needs to be faster. To enable that, retailers need to rely on machine learning and sentiment analytics, this helps them mine actionable insights from large amounts of data from various sources in a short period of time. Applying effective algorithms retailers can leverage the cognitive powers of analytics and take action to resolve issues faster. For instance sending a thank you note with rewards point to a customer who has recently visited and positively reviewed your restaurant would make them more loyal to your brand. This will also help in solving customer issues before they become a negative campaign.
Turn comments and compliments into actionable insights
Sentiment analytics uses natural language processing where the algorithms are taught to find words from text that match with their database of meanings to identify positive or negative emotions. Using the same method retailers can find out the products or services that are garnering more positive or negative responses. For example if a feedback form of a jacket from an ecommerce clothing retailer says “The material is not so good but the delivery was on time.” Then natural language processing of sentiment analytics will match jacket material with negative emotions and delivery service with positive emotion.
- Identify problem areas: Analyzing the above review the retailer gets two vital information. First, that even though the customer is not happy with the overall product, they are still okay with the retailer’s service. 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.
- Define solutions: Taking the above example we can understand that once the problem area is defined actions can be taken to define solutions. For above 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.
What people do and why they do it
Sentiment analytics in retail answers the crucial question of “why” and offers valuable insights into customers’ likes and dislikes. Just knowing that your customer likes your brand isn’t enough. You need to know why they like you so that you may market the “why” more aggressively and strengthen customer loyalty. Does Lisa keep coming to your boutique because your designs are unique? Or is it the best priced store for her budget for designer items? Until you know Lisa’s “why”, you’ll not know “how” to more effectively reach out to her.
Once you know her “why” it is easier for you to devise a strategy to keep her engaged effectively. If her reason is that you provide her best products in her budget then giving her one time offers, discounts vouchers and gift cards that will make you her go to shop.
Why consumer sentiment index is not enough anymore
Retail giants have relied on the results of consumer sentiment index for a long time. It is based on online or offline surveys that takes into account the individual consumers’ financial health and thus measures the overall economic growth. It is a gross generalization based on economic growth. Customer sentiments are more complicated than this. Even with overall economic growth, retailers like Macy’s and Fitbit were forced to lay off people and close stores this year. It is not because individual financial situation, this is because lack of understanding of customer demands. Products and services need to change at a faster pace to keep up with ever evolving customer needs. Customers are more willing to share information about their expectations, satisfaction and dissatisfaction. Instead of generalizing opinions, retailers need to seize this opportunity to listen and understand customer sentiments and act accordingly.
Understanding sentiments and where they stem from
Sentiments are complex and sometimes the words we choose to express them are even more so. Training algorithms to understand human emotions has become a reality with sentiment analytics. At the same time the language processing is still in the initial stages. Whereas deciphering good or bad from a straightforward statement like “I like your food but not the ambience” is easy, deriving meaning from a sarcastic comment or a review filled with pop culture references and colloquial words can be very difficult. That is why sentiment analytics needs the help of data analytics to come to an all-round conclusion or produce the right insights. Applying analytics to customer profiles and then comparing the sentiments with them would give retailers a clearer picture of the scenario and help them create better strategies to build customer loyalty for the long run.
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