With big data, you can observe various patterns and trends associated with your customers' behavior to trigger their loyalty. So, in theory, the more data you have, the more patterns and trends you can identify. The trick is to do it effectively and correctly. Hopefully, the guide below will help with this.
Start by analyzing a few big data sets
Big data analytics can be time-consuming and costly, which is why it's often more efficient to look at a few specific areas (such as the purchasing behavior of your least profitable market segment, for example), at least in the beginning.
Then turn your data into useful information
This is particularly important. The ecommerce development company Iflexion states that data itself "does not create a customer-centered culture." It should be used to create a better user experience for the customer, which can only be achieved by linking the data with customer feedback loops.
For example, data analysis will enable you to identify ...
- Your most profitable market segments.
- The proportion of customers making repeat purchases.
- The proportion of customers spending more than $10, $50, $100, $1,000, etc.
Analyze data for key performance indicators like the above and see how you stand with this against your competitors.
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And how does this help you build brand loyalty?
Well, firstly, let's take a look at operational efficiency – the level at which a business can sell products or services for the lowest cost while maintaining high standards in terms of quality and customer service.
In regard to ecommerce, one way to measure operational efficiency is to analyze the speed of your customers' purchases. There are many ways to look at this. For example, for shoppers on your website, you might look at the time between when they ...
- Arrived at the website and made a purchase.
- Viewed a product or service and added it to a shopping cart.
- Added a product or service to a shopping cart and purchased it.
- Inquired about a product or service and received an answer.
These big data analytics can show you the typical pitfalls in your customers' journey:
- Is there anything that could be improved?
- Is there a particular process that frustrates your customers?
- Are they taking an unusually long time to decide whether to buy some of your products or services?
Or maybe they aren't navigating your website as quickly as they should be (e.g., because the 'next' button is too small or isn't displayed properly)? Worse, if they can't find the information they need, they might be defecting to a competitor's website, which can also be bad for your website's SEO.
Optimize customer experience
The more data you analyze, the easier it will be to establish the causes of any failures or issues that might be compromising your customers' experience. With a more comprehensive understanding of your customers' attributes and behavior, you can also optimize their experience through targeted marketing to increase revenue per customer.
Optimize your pricing
Use algorithms to monitor your competitors' activity and swiftly adapt to new market changes. This will make it easier for you to decide when to adjust your prices to maximize revenue. This needn't be limited to real time; with big data, you can also forecast demand and predict trends so that your pricing is optimized for a maximum ROI at all times.
Optimize your marketing through personalization
Make sure you're sending the right messages to the right audience. With data analysis and timely messages, your customers will only receive marketing communications they are likely to be interested in – which keeps your brand relevant.
Optimize discounts
Every customer loves a good bargain, especially when it's a product they're likely to buy. With big data, you can create personalized coupons at the point of sale, all based on your customers' purchasing history and shopping habits. This reduces the risk of irrelevant promotions that your customers won't be interested in or might not notice.
Optimize your response to errors
Most customers appreciate that even the most successful brands are not immune to occasional errors. What's more important is how you respond to those errors so that you maintain a loyal customer base. Without analyzing data, it's much easier to miss a situation where a customer might not have received something on time or was given incorrect advice or information.
So how do you respond in this kind of situation? Some businesses might apologize and promise to do better next time. Other, more innovative brands will use their big data (such as the number of delayed deliveries, faulty products or staff errors) to adopt the 'smart prescription' approach.
What is the 'smart prescription’ approach?
Whenever something goes wrong, every customer who has encountered a problem of any sort will receive an incentive or reward, such as a freebie or a discount on their next purchase. Each reward will be optimized to the customer to make them feel valued and special. This increases the likelihood that they will remain loyal to your brand.
Using your new insight to create more effective campaigns
Maybe a recent data analysis has unveiled a weakness in your marketing campaigns, or underperformance in your sales for a particular product or service. If so, use this data to develop a new strategy to make improvements.
For example, does your data reveal an imbalance between customer acquisition and customer retention? Are you acquiring many customers but failing to retain them? Or is it the other way around? Is one customer segment spending significantly less than another, or is a particular product or service simply failing to sell?
Once you've found a problem or a weakness, build a new strategy to resolve it. Then, use big data to measure the success of this new strategy.
If it could be summarized in just two statements, these are the main reasons for using big data to build brand loyalty:
- Gain insight into your brand's weaknesses and find out what's reducing customer loyalty.
- Measure your efforts to resolve those weaknesses and increase customer loyalty.
The main challenge is what data to use and how to turn it into actionable information that provides sufficient insights for managers to make informed choices for the future. This way, they can minimize risks and avoid mistakes. Again, this is why it's best to be selective in the number of big data sets you use initially. Focus on getting as much useful information as possible before moving on to more sets.
Boosting your cybersecurity
According to the study Navigating the Cybersecurity Equation by 300Brand organization MeriTalk, more than 80 percent of U.S. federal government agencies reported using big data analytics for their cybersecurity. Of those, 84 percent said that big data helped them thwart a cybersecurity attack, and 90 percent said they had seen a decline in the number of security breaches.
Of course, anyone working in cybersecurity will know that big data itself can be a potential threat if it is not used responsibly. For example, the failure to protect personal details and sensitive information (e.g., customers' addresses and phone numbers) can expose your customers to cybercriminals and put your business at risk. Not having the skill set to interpret data can also be problematic, because there is a risk of producing misleading or incorrect information, which can also jeopardize your business.