Creating the ideal eCommerce customer experience has never been easier for online retailers. Through the use of retail inventory analytics, there are many opportunities for you to increase online sales and customer loyalty.
In the recent past, retailers had to rely on their intuition to guess what customers wanted and to make key business decisions. Even as online companies started to track historical data on their customers journey, they didn’t know how to organize or use this information to their advantage.
This is all changing as eCommerce companies learn to harness the most powerful information that they have: the behavior of their existing customers and potential customers.
As online companies have access to more data, they’re also able to provide a more customized eCommerce customer experience for their potential customers. From relevant social media advertising to a personalized shopping experience, the possibilities are full of potential.
Here we'll look at four innovative ways that data and retail inventory analytics are shaping the eCommerce customer service.
BONUS: Before you read further, download our Inventory Management Software Whitepaper to see how Logiwa uses real-time tracking to help customers get up to 100% inventory accuracy and increase shipments by 2.5x.
Personalization and the Changing Expectations of Your Customer
The data a user generates when they enter your eCommerce site, to the moment they exit, can be recorded. Analytics allow you to see:
- What visitors look at
- If a purchase is made
- Browsing and purchasing habits
The advantage of data use for the eCommerce customer service boils down to one concept: personalization. Data allows you to get to know your customers so you can show them individualized offers, and other content based on their previous product views, past purchases, demographics, and content interactions.
74% of consumers get frustrated when content has nothing to do with them. Personalization can ensure that your offers are relevant to each of your customer segments.
“Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.” - Margaret Rouse, SearchCustomer Experience
You can trace the use of data back to the time of the first computers in the 1960s. Since then, data use has exploded. What started out as a rudimentary device has become a sophisticated system in less than a lifetime. Like computers, mobile technology, and the internet, data has become unstoppable.
Companies started storing customer data long before they could use the information. Cheap cloud storage led to businesses hoarding massive databases of customer interaction for years, also known as data lakes.
The real game-changer in data storage came with the recent rise of cloud-based analytics systems. These systems finally gave companies the tools they need to put customer experience data to use. Robust cloud-based order fulfillment systems harness the information stored in data to provide you with real-time insights about your customers journey.
Before these systems, most analytics platforms were either not feasible or too slow to actually help. Now, with a cloud-based solution, you can easily and quickly adopt technology that can help you create an ideal eCommerce customer experience for your potential and existing customers.
You no longer have to wonder what your customers think of your products or where the future of your company lies. With data analytics, you can give your customers the personalization they expect.
Here are four ways you can improve your customer experience:
1. Chatbots for Simple Customer Service Issues
Artificial intelligence, in the form of chatbots, automated experiences and conversational AI are taking eCommerce by storm. Chatbots are software that converses directly with the customer through a live chat interface. This can either be directly on your website or through platforms such as Slack, Facebook Messenger, Skype, and even Alexa.
The simplest of chatbots scan the user’s inquiries for keywords to answer their questions. Chatbots can also employ AI and machine learning software that creates a more personalized response based on that customer's data. For example:
User: Hey, how fast is your shipping?
Simple chatbot: Hi! Our shipping is 3-5 business days.
AI Chatbot: Hi Sara! Our shipping is 3-5 business days to White Plains, New York.
Although they may both convey the same information, the AI provides a personalized experience for your eCommerce customers.
Some ecommerce chatbots that might work for your store include:
Chatbots give customers 24/7 service without the long waits of the typical human customer support, but they still offer a personalized customer experience.
They never tire, so you don’t risk providing lackluster service. Chatbots take the redundant and tedious job of answering basic questions again and again off your plate. If a request is too complicated for a chatbot to handle, it gets passed on to a human that can help.
Chatbots are growing in popularity amongst retailers and customers alike — 80 percent of brands plan to use chatbots by 2020. Customers also prefer the convenience of chatbots with over half of 1,000 surveyed stating they would prefer a chatbot if it saved them time.
Aerie, the American Eagle Outfitters sister store for lingerie and clothing, implemented a chatbot for their Millennial and Gen Z customers. They used the chat platform Kik, and the bot allows users to look through products based on customer preferences. The chatbot presents a variety of products using “this” or “that” scenarios. The customer chooses their favorite product of the two, and the chatbot stores that data away for future product recommendations.
Customers don’t find too many things more frustrating than finding out the item that they need is out-of-stock. Likewise, it can be potentially damaging for a company to have more stock than necessary.
For too long, retailers had to guess what the demand for their product would be. Now, you can make accurate predictions with the help of data. Demand forecasting uses customer data to predict the market for a product or service. This can help to lower holding costs (or your costs to store inventory), improve efficiency, and lower operational expenses.
Demand forecasting uses data on customer demand, sales and promotions, seasonal fluctuations, and even weather, to create a personalized forecast. It allows you to optimize your stock levels, reorder points and available to promise (ATP).
You can avoid frustrating customers with an "out-of-stock" item and keep your inventory at a profitable level.
3. Dynamic Pricing and Offers
Dynamic pricing is not a new sales trick. The initial concept of “happy hour” in bars was based on the fact that lower prices would draw in customers during slow times. Businesses know that they can maintain the highest profit margin by pricing based on how much the customer is willing to pay.
Data has taken the concept of dynamic pricing to make it more relevant and personalized than ever.
“Simply put, dynamic pricing is a strategy in which product prices continuously adjust, sometimes in a matter of minutes, in response to real-time supply and demand.” Jawad Khan, Business.com
The use of data can tell a retailer, with reasonable accuracy, how much a customer is willing to spend on a product or service. For example, eCommerce retailers can see exactly where a customer is coming from based on their IP address. This can give you information on how affluent the customer is.
In addition to an IP address, data can tell you what device the customer is using to access your website and what that potentially says about the eCommerce customer journey. Orbitz, for example, found that Mac users spent about 30% more on a hotel than Windows users. This led them to advertise more expensive listings to Mac users as opposed to Windows users.
Past customer activity has also led some businesses to scale their pricing. Airlines, for example, infamously charge their frequent fliers more for the same flight. Typically, customers who fly more often do so because of business, which means that airlines can charge them more because it’s more likely that the flight isn’t optional for those customers.
Dynamic pricing is a double-edged sword for customers. It can give them the information that they want more quickly. With Orbitz, for example, customers were guided to the most relevant information for them. It also allows new customers to try a brand for less risk. However, it can end up charging them more based on data about how much they could potentially spend.
Automate jobs within your fulfillment center – either through robotics, smart batching
Some of the top rated dynamic pricing software on the market include:
- Wiser Solutions: “Wiser helps retailers and brands turn omnichannel data into action that increases revenue, reduces costs, and improves marketing effectiveness.”
- Price f(x): “Our products aim to accomplish same or more than those of our competition but with much less complexity – intentionally (!). We build products that are intuitive, work smarter, feel better, allow you to do things your way, making your professional day a “better day”.
- Competera: “Competera is a cloud-based pricing platform for retail teams. It leverages the power of neural networks to provide retailers with the best-in-class product data for the full market visibility and timely competitive response.”
4. Suggestive Selling
Predictive analytics, or in-depth analysis of your customers’ browsing and buying habits, can give you better customer insights, ultimately helping you scale your business. It can help you grow in ways that best suit your customers, and allows you to make recommendations based on what your audience likes.
Suggestive selling is growing at an unprecedented pace because the technology is easier than ever to access. Netflix is a perfect example of a company that uses this software. Netflix offered a $1 million prize to anyone who could improve its recommendation engine by 10% in 2009. It took until 2011, but a team finally did it. Netflix paid the prize and promptly tossed all of the code.
Within those two years, the problem that made Netflix offer the prize was no longer a problem. The number of customers exploded and provided Netflix all of the data that it needed. They even used data to create the show “House of Cards,” which was a huge success.
Predictive analytics are even used in brick-and-mortar stores to better market to their customers. Target even infamously informed a dad his teenager daughter was pregnant before she did based solely on predictive analytics.
Data allows eCommerce companies to tailor their suggestions so that customers can find a product that suits them faster than ever. Visitors no longer have to go out searching for what they want: companies bring what they need to them.
Amazon has mastered the art of personalized selling through its suggestive selling features. Customers are able to see relevant products while shopping, either as a price comparison or as companion selling items. This creates an easy customer experience.
Echo Look is just one example of the power of their data analytics. It takes a picture or video that is stored to offer recommendations based on trends and what flatters you.
With the data they gather from the camera, they can create a more personalized shopping experience for their customers.
Predictive analytics means that customers can see products that best match them. Data gives them the personalized customer experience that they have come to expect.
How Do You Implement Suggestive Selling on Your Site?
Suggestive selling is the best way for eCommerce businesses to inspire action without a hard sell. Customers get annoyed when they are advertised products that are not relevant to them. With data, you can deliver accurate suggestions based on what your customers look at and buy.
Most eCommerce websites allow you to place “related items” modules on your product pages. There are some considerations you should keep in mind if you choose to use them:
- Don’t just put the module on the page without explaining to your customer how the items are related. It can be something simple like “Do you also need….?” or “Perfect accessories for…”
- Show the price of the suggested products so that customers know if the items are in their budget.
- Be cognizant of how your products go together. If your customer puts diamond earrings in their shopping cart, then they may be interested in a matching necklace, but probably not another pair of earrings.
You may also consider sending a time-limited upsell to your customers once they’ve placed their order. You encourage an additional sale by putting a time limit on it, and make the most of your warehouse picking, packing, and shipping by increasing the items in a single order.
Customer Data for a Better E-Commerce Customer Service
From cameras, pricing, stock, and more, eCommerce has all the information it needs to create an ideal eCommerce customer experience. Customers can get personalized and relevant content, better eCommerce customer service, and avoid some of the common frustrations of online shopping.
The question isn’t whether you have the information that you need to create the ideal customer experience. The question is what are you going to do with all the information that your customers give you?
Written by Ruthie Bowles
Ruthie is a content marketing consultant for Logiwa. Her specialties include small business development and inventory management.