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Businesses are getting ready for this year’s Black Friday and Christmas shopping season. Leading retailers attempt to predict the hottest trends, define reasonable prices and foresee a time-slot an average consumer will spend on a particular product. With big data analytics and powerful machine learning tools, their predictions will be the most accurate.

The term “big data” has become a buzzword for sales teams across nearly every industry over the past few years. Companies have collected vast amounts of data from leads and transactions which no single person would ever be able to process. According to an MIT Sloan Management Review survey of companies earning 500M+ in sales, at least 40% of companies are using machine learning tools to increase performance. From providing insights on leads to recommending current customers new products, machine learning can revolutionize the sales industry in several ways.

1. Added Customer Support

According to Salesforce’s Adam Lawson, customer experience is the most important variable separating successful and unsuccessful sales teams. ML will allow for significant improvements to customer experience — with the ability to proactively follow up with leads, customize the user’s experience and answer questions via chat (called chatbots), every customer will have an experience tailored to their preferences and needs.

2. Improved Forecasting

Within the last few year, advanced lead scoring has become an extremely popular tool for sales teams. Lead scoring, which uses ML, looks at collected data on prospects, such as their budget, size, past sales, and interaction with marketing emails then formulates a score which will project interest and the likelihood of a sale. This process reduces the number of dead leads and focuses a sales team on converting strong leads to clients.

3. Personalized Suggestions

In the retail industry, you may have noticed the text “other customers purchased” or “you may also be interested in,” followed by a list of similar or complementary products. These suggestions are thanks to ML, which allows supporting a consumer-centric approach, through the analysis of sales patterns, purchase histories, and data consumption. Companies like Amazon, Spotify, and Netflix are already employing this solution to suggest additional content for customers. As this technology becomes more readily available, smaller retailers and SaaS companies will begin to follow suit.

In the sales industry, ML can streamline the entire consumer relationship from the first point of contact to customer support. As machine learning continues to improve both sales teams’ and customers’ experiences, its influence over the sales industry will only increase over the next few years.

Want to keep your sales high even after the holiday buying boom is over? Contact us at ELEKS. We’ll make sure your team is equipped with the machine learning tools your company needs to get ahead.

Originally published at eleks.com on November 9, 2017.

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Soft Pasture

By Ben Dickson. This article originally appeared here.

The Internet of Things (IoT) is one of the most exciting phenomena of the tech industry these days. But there seems to be a lot of confusion surrounding it as well. Some think about IoT merely as creating new internet-connected devices, while others are more focused on creating value through adding connectivity and smarts to what already exists out there.

I would argue that the former is an oversimplification of the IoT concept, though it accounts for the most common approach that startups take toward entering the industry. It’s what we call greenfield development, as opposed to the latter approach, which is called brownfield.

Here’s what you need to know about greenfield and brownfield development, their differences, the challenges, and where the right balance stands.

Greenfield IoT development

In software development, greenfield refers to software that is created from scratch in a totally new environment. No constraints are imposed by legacy code, no requirements to integrate with other systems. The development process is straightforward, but the risks are high as well because you’re moving into uncharted territory.

In IoT, greenfield development refers to all these shiny new gadgets and devices that come with internet connectivity. Connected washing machines, smart locks, TVs, thermostats, light bulbs, toasters, coffee machines and whatnot that you see in tech publications and consumer electronic expos are clear examples of greenfield IoT projects.

Greenfield IoT development is adopted by some well-established brands as well as a lineup of startups that are rushing to climb the IoT bandwagon and grab a foothold in one of the fastest growing industries. It is much easier for startups to enter greenfield development because they have a clean sheet and no strings attached to past development.

But it also causes some unwanted effects. First of all, when things are created independent of each other and their predecessors, they tend to pull the industry in separate ways. That is why we see the IoT landscape growing in many different directions at the same time, effectively becoming a fragmented hodgepodge of incompatible and non-interoperable standards and protocols. Meanwhile, the true future of IoT is an ecosystem of connected devices that can autonomously inter-communicate (M2M) without human intervention and create value for the community. And that’s not where these isolated efforts are leading us.

Also, many of these companies are blindly rushing into IoT development without regard to the many challenges they will eventually face. Many of the ideas we see are plain stupidand make the internet of things look like the internet of gadgets. Nice-to-haves start to screen out must-haves, and the IoT’s real potential for disruption and change will become obscured by the image of a luxury industry.

As is the case with most nascent industries, a lot of startups will sprout and many will wither and die before they can muster the strength to withstand the tidal waves that will wash over the landscape. And in their wake, they will leave thousands and millions of consumers with unsupported devices running buggy—and potentially vulnerable—software.

On the consumer side, greenfield products will impose the requirement to throw away appliances that should’ve worked for many more years. And who’s going to flush down hundreds and thousands of hard-earned dollars down the drain to buy something that won’t necessarily solve a critical problem?

On the industrial side, the strain is going to be even more amplified. The costs of replacing entire infrastructures are going to be stellar, and in some cases the feat will be impossible.

This all doesn’t mean that greenfield development is bad. It just means that it shouldn’t be regarded as the only path to developing IoT solutions.

Brownfield IoT development

Again, to take the cue from software development, brownfield development refers to any form of software that created on top of legacy systems or with the aim of coexisting with other software that are already in use. This will impose some constraints and requirements that will limit design and implementation decisions to the developers. The development process can become challenging and arduous and require meticulous analysis, design and testing, things that many upstart developers don’t have the patience for.

The same thing applies to IoT, but the challenges become even more accentuated. In brownfield IoT development, developers inherit hardware, embedded software and design decisions. They can’t deliberate on where they want to direct their efforts and will have to live and work within a constrained context. Throwing away all the legacy stuff will be costly. Some of it has decades of history, testing and implementation behind it, and manufacturers aren’t ready to repeat that cycle all over again for the sake of connectivity.

Brownfield is especially important in industrial IoT (IIoT), such as smart buildings, bridges, roads, railways and all infrastructure that have been around for decades and will continue to be around for decades more. Connecting these to the cloud (and the fog), collecting data and obtaining actionable insights might be even more pertinent than having a light bulb that can be turned on and off with your smartphone. IIoT is what will make our cities smarter, more efficient, and create the basis to support the technology of the future, shared economies, fully autonomous vehicles and things that we can’t imagine right now.

But as its software development counterpart, brownfield IoT development is very challenging, and that’s why manufacturers and developers are reluctant and loathe to engage in it. And thus, we’re missing out on a lot of the opportunities that IoT can provide.

So which is the better?

There’s no preference. There should be balance and coordination between greenfield and brownfield IoT development. We should see more efforts that bridge the gap between so many dispersed efforts in IoT development, a collective effort toward creating establishing standards that will ensure present and future IoT devices can seamlessly connect and combine their functionality and power. I’ve addressed some of these issues in a piece I wrote for TechCrunch a while back, and I think there’s a lot we can learn from the software industry. I’ll be writing about it again, because I think a lot needs to be done to have IoT development head in the right direction.

The point is, we don’t need to reinvent the wheel. We just have to use it correctly.

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