Significant growth in competition, multiple options available to customers coupled with the explosive growth in digital information have created a potential challenge of information overload.
There is need to filter, prioritize and efficiently deliver relevant information to users to alleviate this problem.
Thus, the need for prioritization and personalization.
Increased demand for recommender systems more than ever before
Recommender systems solve this problem by analyzing large volume of data to provide businesses and users with personalized content and services according to their preferences and tastes.
Such services assist CPG distributors and e-retailers to boost sales and increase the number of returning customers.
We at Quadratyx provide recommendation engines for the ever-evolving B2B & B2C selling space. Our recommendation engine analyzes customer behavior, order history, and similar shopper intent.
You could be a distributor for CPG products, a retail store or an e-commerce player, our recommendation engine will ensure you have a trusted advisor to help you along the way.
By stocking or displaying most relevant products, we make the shopping journey more personal for your customers.
Happier customers mean more repeat visits, higher order sizes and, most importantly, more sales. Our personalized product recommendation solution has helped clients, across the globe, achieve improved customer shopping experience, increased conversion rates and average order size and boost in total sales by up to 40%.
How can B2B marketers benefit from personalized recommendation, you may ask?
In B2B the sales cycle is generally longer, more people are involved and the support ‘ecosystems’ play a key role. Our recommendation engines have assisted salesmen of a FMCG distributor, to deliver the right order at the right time to their customer (in this case, a retail store).
Our B2B product recommendation tool would learn from past behavior exhibited by a retail store including other important business characteristics and then make recommendations accordingly.
Our process
Purchase Recommendation Services We Offer
Within the B2C space, our recommendation solution can assist you to
- Understand your customer’s experience with your firm and brand,
- Optimize your customer journey to shorten the buying cycle
- Reduce churn and
- Create exceptional customer experiences
- Customer Lifetime Value (CLV) Prediction
- Omni-channel experience
Within the B2B space, our Purchase Recommendation solution recommends ideal outlet purchase behavior and uses a combination of advanced Machine Learning algorithms to achieve accurate results.
Our dynamic purchase recommendation services also includes:
At the location level, segmenting a store allows distributors and marketers to plan their merchandising, their assortment of products, and their marketing efforts for greater efficiency and effectiveness.
Brands and retailers need to understand how each of their stores will perform, based on different factors. For instance, the site’s location (high street, residential, retail park), nearby proximity drivers (transport hubs, attractions, transient work force), local competitors and the consumer profile within catchment. Optimizing the entire supply chain network can produce impressive results when paired with precise insight from data analytics.
Accurately forecasting future demand at a very granular level – per SKU, per day, per location. Our Artificial Intelligence (AI) solution delivers probabilistic forecasts for automating daily replenishment decisions across products and stores based on hundreds of different factors including weather, promotions, festival seasons, sales patterns in nearby stores, etc. Our solution also enables inventory optimization based on a retailer’s stock management policies.
Quadratyx’s self-learning recommendation engine works in real-time, detecting product and customer behavior updates as they happen and updating recommendations accordingly, ensuring a smooth, up-to-date and relevant user experience.
Leverage Route Optimization
Advanced machine learning techniques, big data technologies, genetic algorithms, and data engineering is used to plan the most optimal route for a salesperson while keeping the factors like customer delivery schedules, transportation method, time, and costs into consideration. Our dynamic route sequencing helps you to choose the best possible routes for your deliveries, thereby reducing delivery time, and improve your bottom line.
Our 4 - step Purchase Recommendation Process:
Phase 1
Fetch data from database. Blend and transform data.
Phase 2
Develop algorithm to find similar stores.
Phase 3
- Feature extraction from transactions.
- Feature matrix training
- Validate results to choose the best model
Phase 4
Best performing model will be used to generate a recommendation list for a given store
Software we leverage
One of the main offerings under our recommendation solutions repertoire is our proprietary solution Purchase Recommendation Engine for Businesses (PRE-B™), a B2B machine learning solution for recommending an ideal outlet purchase behaviour for a retailer. It is built by a team of experts in Big Data Technologies, Data Sciences and Software Engineering.
Our technology depth covers big data solutions and cloud hosting of SaaS services, image and text based mining solutions, end-to-end ML based automation solutions, cognitive computing and deep learning, and predictive analytics. For our recommendation solutions, we also leverage the following tools to provide you with the best possible product recommendation services:
