How Big Data Analytics can Improve Customer Loyalty
In the current market environment, the customer is key, and consumer loyalty is an essential business aspect that must be managed in order to be successful. It is no longer sufficient to have a good product or service, especially when it is simple for someone to search the internet for other options. If your business […]
In the current market environment, the customer is key, and consumer loyalty is an essential business aspect that must be managed in order to be successful. It is no longer sufficient to have a good product or service, especially when it is simple for someone to search the internet for other options. If your business does not make an effort to attract and keep customers, they will be drawn away by competitors that offer higher-quality products, lower prices, or a better experience. Regardless of whether you are in the retail or service industry, customer experience needs to be a priority.
This also means, however, that companies have an additional avenue through which to attract customers. Even if you do not have the best product or service at the lowest price in the market, you can win people over by treating them well and showing them targeted promotions. Additionally, by focusing on attracting the customer rather than promoting one product, it is easier to upsell and cross-promote, leading to a more efficient usage of marketing funds. To achieve all of this, big data analytics is invaluable.
Big data analytics can be used for a variety of different functions:
- Track and analyze customer events
There is a wealth of data that can be collected from customer interactions. It is possible to track things such as dropped calls, delayed flights, site errors, and many others. Analyzing data from these events makes it easier to identify problem areas as well as successes so that the company can determine where it should focus its efforts.
- Predict future behavior
Predictive analytics can take data from past incidents and determine how customers will react to similar problems in the future, as well as offer suggestions for the best way to respond. This provides insight into how likely a customer is to leave, give a bad review, or perform some other negative action in response to a negative experience. It even makes it possible to create automated responses to certain situations, such as offering coupons or other compensation.
- Identify key customers
It can be difficult to determine which new customers are likely to return, and who to focus resources on to attract them. Advanced analytics services can find patterns and draw conclusions that individuals cannot, allowing the company to target consumers more precisely.
Making the most of big data analytics
Simply collecting data is not enough, nor is analyzing it. Companies need to act on the data collected and evaluate the results. These tools also should not be used in isolation: talking to customers is still invaluable, as it can yield information that analytics will not pick up on, and will make people feel more valued than automated responses do.
Additionally, marketing analytics services and tools need to be able to adjust to changing conditions. It is not enough to set something up once and leave it forever. Consumer actions and preferences change all the time, as does technology. If there is a new type of data to be collected, such as from a new social media platform, or a change in how the company operates, the analytics need to change to accommodate this.
Finally, analytics should never hinder the customer’s experience. If they make it harder for the customer to navigate a site, create slow load times, or force the customer to do something they don’t want to, analytics are getting in the way rather than helping. A tool to increase customer loyalty should not create negative customer experiences.