7 ways to improve your eCommerce customer data collection

In the last year, businesses have lost $756 billion because of poor eCommerce personalization. 

Customers have become accustomed to a tailored digital service. Unless you start gathering data about your customers and improving their online shopping experience, customers will walk their wallets to your competitor, who will offer them a better service.

How customer information can be collected and used?

We will look at 7 use cases of how to better collect customer data in your eCommerce and put it to good use. 

Don’t worry. With each use case, we will break down the customer data you need to unlock an advantage over your competitors and how to best gather the customer information.

1. Lower cart abandonment

Online shoppers can be fickle. They browse, add items to the shopping carts, but then leave without even saying goodbye. 

Statista reports that 88% of online shopping orders were abandoned. Aka, almost 9/10 shoppers leave their carts without purchase.

Converting more browsers into shoppers increases your bottom line, without adding to your customer acquisition costs.

What customer information do you need to lower cart abandonment?

1. Start by establishing a baseline. Use Google Analytics Enhanced Ecommerce to automatically track customer behavior and collect checkout customer information. The Checkout Behavior Analysis Report shows you how customers move from one step of your checkout page to another and shows the current conversion rate. This is the conversion rate you need to improve.
2. Understand the online shopping experience. Once you have the baseline, understand what drives customers away from your checkout page. Implement tracking tools like Hotjar that measures customer interaction in real-time. Use heat maps and session recordings to understand where visitors are clicking and what information is missing. If you see multiple clicks on the “return policy” you need to communicate more clearly how returns work. If you see people hovering over estimated delivery times and leaving, you need to reconsider if the speed of your shipping is fast enough to compete with Amazon-like one-day deliveries.
3. Wow with personalized content. Show personalized offers with discounts or specific items the visitors left in their cart using tools like OptinMonster once the visitor returns to your e-shop. Personalizing content can increase your revenue by 18x. And the tools take care of the gathering consumer data themselves. They track items added to the cart, as well as which visitor is returning.

2. Improve your product offering

Nothing sells better than a good product. To choose better products for your e-commerce business, you need to gather the right customer information:

1. Analyze transactional data. In-demand products are sold products. Look at how frequently a product was purchased based on customer purchase orders. You do not need to gather any additional customer information. Your online retail store does that in the backend automatically. Make sure to stock up on your best-sold items.
2. Collect social media information. Customers discuss their top purchases on social media. From images on Instagram to shoutouts on Twitter. Use tooling that automatically collects the information about a product brand and what customers are saying. Once you have the data you can perform sentiment analysis over the text to establish if a product is loved or hated.
3. Gather reviews. If you do not already offer customers review options for your e-store, check your competitors. Looking at reviews can tell you if customers are happy with a product or just meh. This is an excellent source of customer information to discover new potential winners.
4. Collect customer feedback. Products might sell well, but without customer feedback, you cannot tell if your customers are happy with the product they bought. Some of the best-sold products can cut into your margins if consumers return them en masse because of dissatisfaction. Send out customer surveys to understand how happy customers were with their purchases, and whether you should keep offering a product.

3. Personalize product suggestions

Recommended products are old news for eCommerce businesses. On product pages and checkout pages, you can often find additional product suggestions like “Customers also bought” or bundle offerings pairing the chosen product with similar ones.

The problem? The product suggestions are seldom personalized. And that ticks customers off. According to Janrain, 74% of people hate being shown irrelevant content in personalization attempts.

So. which data do you need to collect to offer better personalizations?

1. Start with individual shopping habits. When recommending products, first look at what that specific customer bought in the past. For example, if Anna bought a T-shirt next to pants in the past and only has a T-shirt in her cart today, suggest she adds a pair of pants as well. Apply the recommendation with some common sense. No one wants to repeat the fiasco where a customer who bought a toilet seat was automatically recommended to buy several more. You already collected the data in your order database, you just need to put them to good use by building artificial intelligence models for individual recommendations.
2. Recommend based on segments. One-size-fits-all recommendations seldomly work. For example, if you retail drinks and see that the two most purchased items are green leaf tea and sport isotonic drinks, you would not recommend those two together. They are two different personas who buy those drinks: one is a sports enthusiast, while the other is a classic tea lover. For this type of recommendation you also already have collected data, but you need to gather it in such a form, so it can be used for recommender systems. Use machine learning algorithms to segment product purchases into segments, and recommend the product to the relevant customer segment. 

4. Offer better customer service

Why is eCommerce customer service important? Because 84% of customers said customer service is one of the key factors helping them decide whether to purchase from a company or not.

So, what customer data do you need to collect to improve customer service?

1. Gather common customer queries. When the same question is asked of your customer team multiple times it means it is better answered elsewhere. Instead of wasting time writing the same answers repeatedly, create a knowledge base or FAQ with the most frequent questions and answers.
2. Survey your customers for feedback. 92% of consumers stop purchasing from a company after 3 poor customer experiences. Asking customers how satisfied they are with a complaint resolution helps you understand where you are excelling at customer service and where your team needs to improve. It also lets customers know that you care about their opinion, which increases customer retention and loyalty.

5. Optimize your product availability

Nothing gets customers more disappointed than looking for a product they want to buy and seeing those dreadful words “out of stock”.

Stop yourself from leaving money on the table by anticipating when demand for a product will rise and stock up ahead. 

Predictive analytics allows you to anticipate the rise and fall in customer demand and keep your inventory at a ready.

What data do you need to gather from your customers to create predictive analytics models?

1. Past order purchases. History can be a great teacher. If we listen. Customers often repurchase with a specific pattern. If they love a specific grind of coffee, you can expect them to repurchase whenever their provisions run out. Let us say Luke drinks 3 cups a day, so he orders a new batch every 2 weeks. Monika, on the other hand, is more of a coffee-as-a-treat kind of person, so she repurchases every 3 months. Gather the historic purchase frequency of your individual customers to understand when they will be likely to repurchase. Combine the individual predictions to understand when you need to re-stock. 
2. Seasonality and trend data. Customers open their wallets wider at specific times of the year. Seasonal holidays, Black Friday, and Cyber Mondays are just some of the common examples. But industries and products have their seasonality as well. Skis sell best during autumn and water sport retailers see their orders rise with spring. Collect data from your e-shop and other online retailers to understand when customers are more likely to buy certain products.

6. Improve customer retention

Acquiring new customers is more expensive than retaining existing ones. 

How can you identify loyal customers and make the most out of that data?

1. Start by establishing a baseline. Kickstart your customer retention data gathering by understanding who your most loyal customers are. Collect information on repeat purchases and segment your customer base in at least 3 buckets: (1) new customers, (2) long-term customers (the loyal ones), (3) churned customers (aka should have re-purchased, but have not). Looking at the ratio between the three buckets will help you determine your long-term trends. Ideally, your customer base should be growing and retained customers should be bigger than churned ones. 
2. Dig deeper with surveys. Once you identify which customers are loyal and which ones have stopped purchasing from you, you can send your customer base a Net Promoter Score (NPS) questionnaire. It will help you understand who among your loyal customers is a promoter of your brand. Really dig into that satisfied segment. Understanding them helps you tailor your product offerings to the highest payers as well as gauge the ideal characteristics you should look for when acquiring new customers.

7. Attract better customers

Customer acquisition can be challenging. Turning website visitors into new customers involves a combination of advertising and conversion rate optimization skills.

We have touched upon conversion rate optimization when we talked about lowering the cart abandonment rate.

But when it comes to advertising, data gathering can be your friend:

1. Install tracking software from advertising platforms. Facebook pixel and Google Ads both offer free software which helps you track and collect data about users, who clicked on your ads. There are multiple ways to implement customer tracking on your e-commerce site. But whichever way you chose, the tracking software will give you insights into important metrics that help you understand your customer journeys. You will answer questions such as:

1. How many users clicked on an ad, landed on my product page, but then bounced?
2. Which audiences convert from visitors to buyers?
3. Which ads perform better for customer acquisition?
4. Etc. 

But the tracking software does just give you insights. It also helps you automatically optimize your ads. By collecting data about which visitors converted into buyers, Facebook Ads and Google Ads fine-tune their machine learning algorithms and showcase your ads to people who are more likely to purchase. 

2. Gather customer data into better-performing audiences. Both advertising platforms allow you to collect additional customer data and use it to acquire new customers cheaper. With Google Ads’ Customer Match and Facebook Lookalike Audiences, you can pair the information gathered via tracking pixels with your backend information (e.g. customer emails, customer addresses, average order value, …) to create better-performing audiences for your advertising. This will lower your customer acquisition cost and show your ads to more qualified leads.

Bringing it all together: how to improve eCommerce performance by gathering customer data

Above we showcased the potential impact customer data has on your eCommerce performance when you collect the right customer data.

But data collection has multiple challenges:

• You have to set up collection practices and pipelines from multiple data sources and tools. 
• You have to monitor data collection to check if it is collecting data correctly.
• After data is collected, you often have to clean it. 
• Customer data is often dispersed across multiple sources. Before you can use it in your advertising optimization or product recommendations, you have to join the sources together.

The best practice to automate all your data collection is to rely on data platforms that do the heavy lifting for you.

Keboola can help you gather customer information and extract value for your eCommerce.

Keboola is built as an end-to-end data platform, which allows you to:

1. Automatically collect data from multiple tools with a couple of clicks. With its easy to use interface, you can:

1. Collect data from your digital advertising platforms (Facebook ads, Google ads, Bing ads, Linkedin ads, etc.).
2. Extract social media data such as likes, shares, and follows across multiple platforms (e.g. Instagram, Facebook Pages, Twitter, Snapchat) and keep track of your audience.
3. Capture data about your email campaigns and conversations with ease - just use Keboola’s devoted extractor components for Sendinblue, Mailgun, Mailchimp, ActiveCampaign, and more.
4. Take control of your sales, support, and CRM data by moving it from the platform to your in-house database, which can be done using the Pipedrive component, the Hubspot extractor, Intercom, and many more. Take it a step further and use B!Q Deal Predictions to automate prospect and lead qualification using machine learning. 

1. Bring all your data into a single database, so it can be more easily analyzed. 
2. Use the analytic and machine learning tools to create advanced analyses, such as AI-driven product recommendation, predictive analytic to inform you when your product needs to be re-ordered with suppliers, build customer churn and customer loyalty models, etc.
3. Send all the data to other tools which put it to use or use dashboarding software so you can keep track of your KPIs and metrics in real-time.

Try it for yourself. Keboola offers a no-questions-asked, always-free tier, so you can play around and tap into the potential of bringing all your eCommerce data together.