You get
Bigger carts
The customer finds the product that completes the purchase in the cart, at the moment they are ready to buy.
For every customer, the right product. Chosen by the numbers.
RecommenderPro shows every customer the products that customers like them chose: in the cart, in the email, in your agents' app. Fuller carts, customers who come back.
Every cart gets completed with the product it is missing
The customer's cart contains
- Espresso machine
From your products, RecommenderPro suggests
- Coffee beans, 1 kg 84% 84 of 100 customers with purchases in common bought it
- Descaler 61% 61 of 100 customers with purchases in common bought it
- Set of 6 espresso cups 47% 47 of 100 customers with purchases in common bought it
The customer's cart contains
- Trekking backpack
- Hiking boots
From your products, RecommenderPro suggests
- Technical socks 78% 78 of 100 customers with purchases in common bought it
- Stainless steel water bottle 69% 69 of 100 customers with purchases in common bought it
- Telescopic trekking poles 52% 52 of 100 customers with purchases in common bought it
The customer's cart contains
- Laser printer
- Toner
- A4 paper, ream
From your products, RecommenderPro suggests
- USB cable 74% 74 of 100 customers with purchases in common bought it
- Adhesive labels 55% 55 of 100 customers with purchases in common bought it
- Ring binders 39% 39 of 100 customers with purchases in common bought it
By the numbers, not by gut feeling. The product that customers with purchases in common buy most always comes first.
It changes with the seasons. Pairings follow the purchases you load: update your history and they are recalculated, with no rules to rewrite by hand.
Illustrative example, with made-up data.
Today you suggest to customers
These are guesses. The customer can tell, and buys only what they came for.
The revenue you are missing sits in carts that are already closed
Missed opportunity
The second product they never saw
The customer closes the order without knowing that the accessory, the spare part or the pack they would gladly have bought even existed. You will never know.
Missed opportunity
The same email to everyone
One newsletter for a thousand customers tells each of them you do not know them. Their next order may go to someone who does.
Missed opportunity
Agents who start from the price list
An agent's experience is valuable, but nobody can remember the purchases of thousands of customers. Your order history can, and today it sits idle.
The big e-commerce players have known for years, and the numbers show it
35%
of purchases on Amazon come from a recommendation. On Netflix it is 75% of what people watch.
+11%
in sales thanks to recommendations, in a study of more than two million page views.
32%
of Black Friday 2017 digital revenue came from the 6% of shoppers who used recommendations.
Salesforce, press release, 28 November 2017
58%
of consumers are more likely to buy from a shop that suggests options tailored to them.
These are studies on recommendation systems in general, not results measured on RecommenderPro. The effect on your revenue depends on your order history and your catalogue.
The same effect, on your order history
RecommenderPro turns the orders you already have into suggestions, at the points where your customers decide.
In your online shop.
The cart fills up.
Before paying, the customer sees what customers like them bought together. Fewer single-item orders.
In newsletters.
Every email carries a selection of its own.
Each customer receives products based on their own purchases, not the same ones as everyone else.
On visits and at the counter.
The agent arrives prepared.
Before meeting the customer, the agent knows what to offer. The conversation starts from a proposal, not from the price list.
It works even for customers you don't know
Two ways to ask for a suggestion, depending on what you know about the customer. Use the one you need, or both.
History
You know the customer
Start from everything they have already bought
In the customer account area, on the phone, in the shop with a loyalty card, on a visit with the agent: each customer sees the products missing from their history, chosen by customers who bought like them.
Cart
You don't know who they are
What they have in the cart right now is enough
Even a new visitor with no account gets a suggestion, with one or more products in the cart. With the cart still empty, they see the products that sell best overall.
A different email for every customer
For each customer RecommenderPro prepares the products to offer, up to five each. You import them into your email marketing tool and the same newsletter reaches each person with their own product.
- The right product for each customer, chosen from the purchases of people like them
- You decide the minimum threshold and how many products per customer
- Sending stays in your email marketing tool
To: Customer A Bought an espresso machine
The right coffee for your machine
In the email Coffee beans, 1 kg
To: Customer B Bought a trekking backpack
For your next outing
In the email Stainless steel water bottle
To: Customer C Bought a laser printer
Ready for your next print job
In the email Replacement toner
Illustrative example, with made-up data.
You connect it to what you already use and you decide where it shows
RecommenderPro does not ask you to change anything. It takes the purchases from your ERP or your online shop and returns the list of products to offer. Where and how to show it is up to you.
What you already use
- Your ERP
- Your online shop
- A CSV file of orders
RecommenderPro
Works out what to offer each customer and each cart
Where you show it, your choice
- Product page
- Cart
- Checkout
- Email after the order
- Newsletter
- Agents' app
- Call centre
Any ERP or online shop
It only needs to export purchases and make an HTTP request. We look after the connection.
You design the buying journey
You pick the points where a product is offered: product page, cart, checkout, email after the order, agent visit.
You set how much and for whom
You set the minimum threshold and the number of suggestions, and you can keep separate archives by season, department or type of customer.
What you get
You get
Customers who come back
65% of consumers are more likely to shop with a retailer that knows their purchase history (Accenture, 2016). With suggestions your shop gives that impression.
You get
Fewer rules to write by hand
No more deciding and updating "customers who buy X see Y". Pairings are recalculated from the purchases you load, so they follow seasons and trends.
You get
Your data, little to hand over
Customer and product are enough. Names, addresses, amounts and dates stay in your ERP.
To start, the history you already have is enough
What is left for you is small. We work through the rest with you.
You give us the orders
An export from your ERP or online shop: customer and product, one row per purchase.
We turn them into suggestions
RecommenderPro processes them and makes them available through the web app and the API.
You see them where you sell
In the cart, in emails, in the agents' app. We work with you on the integration, with training and support.
It works best when
Suggestions come from comparing customers: the more orders you have, the more precise they are. If you are not sure your history is enough, the demo will show you.
- You have a large purchase history with many different customers
- Every purchase is tied to a customer: account, loyalty card or customer record on the order
- The catalogue is large enough to have something to suggest
- You can connect your online shop or ERP, or have whoever looks after them do it
Find out what it would suggest to your customers
In a demo we show you the web app and how it connects to your online shop or ERP. If you decide to start, we work with you on the integration.