Machine Learning

Machine Learning has been around since the 1950's. Yes, it is really that old and has fallen in and out of favor over the last 7 decades. We don't focus on the hype but rather on using Machine Learning as a tool in our arsenal. We've had great success integrating both old and new Machine Learning techniques and stay on the cutting edge of available algorithms and learning models. We built some of the first sentiment analysis algorithms, support vector machines and are currently pilot testing SLIDE algorithms. We won't build complicated and cool sounding algorithms just for the sake of it, though. We only recommend and implement Machine Learning algorithms when it is suited to the job at hand and provides definitive results. Below are some examples of Machine learning implementations we've completed.

Numbers and data are how we make clients successful. We analyze clients data to impower them, and lead them to make more informed decisions. In one extraordinary case, we had a client that had built an incredible data warehouse. This data solution was created simply because their competitors in the industry were doing the same. They had little interest in actually using the system. We did and leveraged their system to provide fantastic insights, but still couldn't get them to acknowledge the results or the importance of their data. As an add on to their system, we developed a simple analytics dashboard of some of their key metrics combined with the Machine Learning results. They could not believe the reports and started using the dashboard on a daily basis. It has been used to drive several development efforts throughout the company. Some times visualization is as important as the algorithms themselves.

One of our clients exists in an industry without much direct competition. They never had an eCommerce footprint, because they didn't see the need. However, once one of their competitors opened an eCommerce platform, they tried everything they could to bring their own to market. 8 months and $5 million later, they were sitting on a site that generated less than 3% of the company's overall sales. After all of these sunk costs showed so little of a return, they called us in. With a complete overhaul of the site, at a fraction of the time and cost, we increased the platform's sales 500% week over week, making it the largest sales channel in the company. One notable improvement was a Machine Learning address matching tool. It single handedly increased customer matches in their billing system by 125%, allowing for improved in footprint identification, accurate offer pricing and targeted promotions.

Another client of ours had a much different problem. After meeting with them and quantifying their data, we found a lot of their support calls dropping into their sales queues. They had all of the old standard phone solutions, but couldn't manage to keep support calls out of sales. We set up a number of Machine Learning attribution models to find the underlying issues. The problem, it turns out, wasn't their phone support, but their self help solutions. We implemented a series of sprints for their self help platform, each dropping inbound support calls by 50-100 a day. After 6 sprints, they were seeing a consistent 300-600 less support calls a day company wide. This drastically reduced the load on the support queue and thusly alleviated a much larger percentage of support calls from the sales queue.

One particularly successful client already had an extensive eCommerce footprint. It generated about as many sales as their inbound sales calls. They had even automated some of the simpler orders, so no person was involved until fulfillment. We noticed, however, that their platform was far more sophisticated then what they made use of. We linked the platform's data to a Machine Learning algorithms, focusing on automation, and worked with them on finding existing pain points. After four months of development work, we had increased their automated orders by 700% and pushed them to the top sales channel for the company.

Any IT department worth their salt is always monitoring uptime. The last thing anyone wants is for a site, service or feature to go down and to not know about it. We routinely build out automated service monitoring at a functional level (is the service still up), but some of our Machine Learning monitoring is where we see far more granular and interesting results. Using historical data and learning algorithms, we create alerts that trigger when a service goes outside normal operating parameters. These alerts don't indicate a service is down, but that it is not operating normally, which can lead to an outage. One client uses this to huge effect. We have reduced downtime and outages by 400% by triggering these early warnings.