Chatbots, being inertly flexible, can be leveraged not just in customer service but also to make personalized product recommendations based on customer preferences, previous interactions or purchase histories. And so, this article will explore the possible advantages of using a product recommendation chatbot for your eCommerce as well as how to build one.
eCommerce businesses have always been at the forefront of adopting new technologies since they themselves are a product of such development. Hence, it’s no surprise online stores were some of the first to adopt chatbots, especially when it came to customer support and answering FAQs.
Therefore, it has become imperative for online businesses to not just sell quality merchandise but also strive to create meaningful relationships with customers from the first touchpoint to the last. While fostering loyal and trustful relationships is easy from face to face or when managing a small customer base, when your business expands it becomes more difficult.
That’s where chatbots become so imperative and locking them in the small space of customer support means missing out.
Product recommenders are growing ever so important as users demand personalized products and instant care. They are not just about analyzing shopper behavior but also about influencing them to come and buy again.
Why bother with a bot?
Why not stick to a classic product recommender?
Bots use the power of conversation to make customers feel comfortable and satisfy their need for “instant satisfaction” by tending to their queries immediately.
When it comes to product recommendation, they eliminate the necessity for shoppers to scrolling down an endless category page to find what they need. Instead, they use the conversation as a filtering system, bringing the product to the customer.
What’s more, chatbots can also be designed to remember previous interactions and purchases to offer personalized recommendations based on the buyer’s preferences and chat history. Having somebody to advise shoppers along the way, helps to bring them to the final stage of the sales funnel more smoothly.
You might think that customers don’t like to share data, but within a conversation, providing a name or preferences feels much less intrusive. More importantly, it allows you to observe, in real-time, in which part of the conversation users lose interest and fix the flow of the dialog accordingly. A product recommendation chatbot also detects how shoppers react to new products, pricing, discounts… Data that usually gets lost on a static website.
Clicks give you just the numbers, conversations enable you to dig deep and analyze the sentiment behind the actions.
As mentioned, many eCommerce businesses still shy away from bots. Some of the biggest factors influencing them to stay away are the notions of expense and complexity associated with chatbot implementation.
However, the times have changed and the splurge of no-code tools flooded the digital market making it easy for no-coders to implement sophisticated solutions.
One of those tools is Landbot, an intuitive chatbot flow builder and messaging manager that enables you to create bots for your website, Facebook Messenger bots as well as WhatsApp chatbots.
And this is how you can use it to build your very own product recommendation bot…
Naturally, before you can do anything at all, you do need to register and create an account with Landbot. It’s free and no credit card details are required.
After registration, you will be welcomed by the central dashboard.
From here you have two options
- Click the pink button “BUILD A CHATBOT” and start building from scratch
- Click “Templates” in the header menu and start building using a pre-designed template
While templates are great at showing you what’s possible, for the purposes of our tutorial, we’ll start from scratch:
Landbot interface works on the concept of conversational block connected by arrows that you can create and move around as you wish in a drag-and-drop fashion.
For the purposes of this tutorial, we chose “The Honest Amish” beard products brand as our digital guinea pig. So, the first step to start is by redesigning our welcome message.
Click on the message field to change the text and on the little EDIT icon in the GIF to change it to another GIF or image. Of course, the image/gif is not compulsory and you can get rid of it if you deem it unnecessary. However, visual elements do give an edge to the conversation.
Next, we decided to ask the shopper for his/her name to make the conversation more personal. Again it’s not necessary to know one’s name to suggest a product, but it showcases an important feature of the builder – variables.
Think of a variable as a category name that stores a certain category of information. This way, you can make your bot leverage user responses in real-time by implementing the variable in the dialog:
You can check how it’ll work in practice by clicking first SAVE then PREVIEW in the upper right corner of the builder:
In the “Name Block,” the variable is set automatically to @name. That is just to make your job easier. That is why the builder offers different types of question blocks – each comes with a pre-assigned variable to save time (e.g. email block has preassigned @email variable). However, you can create any new variables you deep useful or important.
The important thing is, you can use variables to categorize and export user data to either a Google Sheet or any other CRM tool you are using.
There are two ways you can present your products and product categories. You can use the traditional BUTTONS block:
Which will take this form on the frontend:
Or, you can use the “Picture Choice” block which allows you to use interactive images with titles instead of buttons:
In the front end, the categories will appear as follows:
As you maybe have noticed, you can directly connect the images with the category/product URL so the user can leave the conversation and go directly to the relevant category/product.
Alternatively, after the customer selects a category, they are presented with the products within that category. Here, the same principle applies. You can let shoppers proceed to the product page or him/her engaged within the conversation.
See sample flow from the backend:
Sample flow from the front end:
For the purpose of showing you what is possible to achieve with the builder, we presented a very direct way of suggesting possible products. However, it is usually better to start off with a few guiding questions – especially if choosing your product is intricate or your product offering is way too varied.
For example, our example eCommerce could ask questions about shopper’s beard size, thickness, hair type and most of all – preference. This allows the product chatbot to go straight to a narrow showcase of relevant products.
The best about product recommendation chatbots is that the conversation doesn’t end with the potential customer selecting one product. When the visitor shows interest in a product or decides to add anything to the cart, the bot can react immediately by suggesting other products or better versions of the selected products:
5. Leverage Integrations
For your product recommendation chatbot to work seamlessly, it needs to be integrated with your eCommerce and the tools you use to manage it.
Similarly to flow building, Landbot significantly simplifies the integration process making it accessible to no-coders (or making it really fast for developers) to set up.
The simplest of examples is the Google Sheet setup which, according to your preference allows you to:
- Insert a new row with the data provided to your bot by the user.
- Update a row the data provided to your bot by the user.
- Retrieve (or check against) the data in the sheet.
You can store and update information with user’s every visit, personalizing every exchange. IN practice, if your bot collects the user’s name and purchases storing them in the sheet, next time the bot identifies the user (through cookies or other identifying factors such as email), the conversation can pick up where it left off.