In today’s competitive retail environment, companies must learn to “listen” and interpret the signals that their customers are giving them about their products and services or face being bested by a competitor who is monitoring customer feedback.
That’s according to a Harvard Business Review blog post that suggests that companies add more “signals” to their predictive analytics and data analysis efforts to better predict what products and services will resonate best with customers.
For example, signals may be how a customer pays for a transaction, the time of day she makes her purchase or if she buys more than one item.
“These are known as signals, because they may help predict some future target variable,” according to the HBR article. “It might be, for example, what else you might want to purchase, given a current purchase.”
Companies should consider adding additional signals to reveal new relationships and correlations of the data they’ve never realized before. In addition, more signals can make the inferences a company makes less subject to bias than those they’ve derived from individual signals.
To make data analysis even more advantageous to a retailer, the era of big data now means that companies can add unstructured data – like the comments and posts captured via social media – to boost customer insight.
Because customers and potential customers share so much information through multiple social media platforms, using data analysis to cull through these revelations is essentially “free market research” according to a Business 2 Community blog post.
Collecting and analyzing social media data allows companies to:
- Monitor brand reputation and that of key competitors through the sentiment that customers express related to the brands.
- Quickly respond to customer complaints and questions via social media.
- Cost-effectively gather customer feedback that can be used to hone future product or service offerings.
“Social intelligence can also provide companies with early insight into market trends which they can then use to develop a strategic approach,” the post notes. “For example, social intelligence could be used to notify companies to an emerging trend among their competitors allowing them time to implement a pricing advantage. In turn, these results can be used for their business forecasting, helping the business to predict future requirements which will save them money and also to assess current or prospective future requirements.”
Using data analysis to monitor customer signals is a large part of ensuring that the customer has the best experience interacting with a particular company.
Analysts suggest that companies treat customers like assets to ensure that they can keep their most valuable customers, according to a recent TechTarget article.
To gather feedback from customers in the most effective manner, companies can assemble a cross-sectional team of employees from each department that can identify the key channels – like email and social media – where customers provide signals.
“You can proactively call them, maybe go after a specific set of customers every quarter, or do surveys and quality reviews and predictive analytics,” notes Bruce Temkin, managing partner of research and consulting firm, The Temkin Group.
Jeanne Bliss, founder of customer service strategy firm CustomerBliss, suggests that companies manage their customers across the entire lifecycle – from initial contact, across purchases and referrals, rewards and discounts – just as they manage the lifecycle of other corporate assets.
She recommends categorizing customer assets according to their profitability to the company. By drilling down to analyze each category, companies can learn what makes one customer segment buy more or complain less than other segments.
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