Ah, the Swiss Army knife: looks innocent enough but is a beautiful piece of ingenuity, versatility and efficiency. Even the most basic one comes with a bunch of extras you might never use. But boy, it comes in handy and not just for camping trips. Each attachment has a multitude of conventional and creative uses. An expensive item falls into a crack in the paving and you scoop it out with the can opener. Suddenly, you’re the hero of the day. So much functionality wrapped up in a neat unobtrusive package.
Similarly, Locstat’s recommender system is an unobtrusive light touch on your existing architecture, packed with functionality for conventional and innovative uses. And, like the Swiss Army knife, even the run of the mill has a host of extras built in: churn prediction, customer segmentation and cross-selling / upselling recommendations.
The conventionally less conventional
We’re all familiar with the conventional uses of recommendation engines. They’re now fairly widespread in the online retail and hospitality industries and many loyalty programmes utilise them to elevate sales.
Our holy tech trinity of Complex Event Processing (CEP), graph database technology and a strong recommendation engine provides you with a hybrid multi-dimensional system perfectly suited for accurate and personalised recommendations in these fields.
The unconventional recommender system
Of course, sales don’t just happen in the retail industry – products and services exist in all sectors of the market: entertainment, finance, insurance, manufacturing, etc. A Locstat recommender system will help you to effectively match your offerings to your clients, customising your service and optimising sales.
Then there’s the B2B arena. For example, suppliers to retailers: Locstat’s recommender system can track sales and make ordering recommendations. Machine Learning (ML) algorithms allow the system to adapt to changing circumstances, regular events (e.g. Christmas, change of season, start of school terms) and fluctuating sales patterns.
This ensures that ordering happens timeously and in appropriate quantities for products to fly off the shelves instead of languishing in the stock room. Your suppliers can also make use of this system to get proactive with orders and provide opportune discounts to motivate sales. This kind of two-way communication is a win-win.
Let’s get creative!
“But,” I hear you tech savvy people say, “Locstat’s recommender system is truly an awesome combination! Surely, there are many other uses?” And you’re right. So, let’s get creative!
There are many operational areas where internal company feedback is required: compliance, fraud detection, maintenance, operations, logistics, risk management, etc. Locstat’s graph intelligence platform monitors data from multiple sources (including sensors), identifies patterns and infers meaning. When a rule is broken, e.g. too many hits on a credit card, low pressure on a pump, adverse weather conditions (maritime and agriculture), an alert is triggered.
The recommendation engine takes this information one step further by enriching this with contextualised information about whether to act as well as what action to take. It then pushes suggestions to the relevant departments, e.g. the sales are on, just monitor usage; pump is reaching regular 5,000 hours’ maintenance check, advise early service; storm coming: batten down the hatches and head inland, etc.
How about some specific examples?
There are many different facets to security, from IT to commercial (e.g. copper theft) to residential. Our rules engine can determine high-risk areas, situations and times, then recommend patches to install, where to position CCTV and how to deploy personnel to avert crime.
The majority of schools use platforms for student data such as attendance, marks and so on. Many of these now have parent portals for viewing test results and reports. Regular or dropping attendance rates or marks can trigger an early alert to notify teachers and/or parents and advise on appropriate intervention.
Additionally, with the current global uncertainty around the COVID 19 crisis, online learning can be monitored to ensure sufficient work is available as well as completed. Where learners repeatedly fall below basic expectations, e.g. work submitted, time spent on exercises, marks achieved, the rules engine can alert the relevant parties and the recommendation engine can offer strategies for either remedial assistance or extension.
Medical insurance schemes gather data from many different aspects of their members’ lives, e.g. medication used, state of general health, visits to doctors, pharmacies and gyms. Monitoring all of this information, Locstat’s recommender system can suggest early interventions, e.g. increase your exercise, see the doctor, as well as validating and enhancing scheme-linked programmes that reward healthy living.
Data feedback from sensors in fields and greenhouses can monitor a myriad of environmental factors such as temperature, humidity and soil content. Detrimental fluctuations trigger rules that alert the farmer and recommendations are provided to advise on best practice.