“Data wins arguments” is a phrase that is frequently thrown around in Silicon Valley. It was originally made famous by Facebook’s then CIO Tim Campos. It perfectly describes the ethos of today’s technology companies like Facebook, Amazon, and Netflix, which religiously use data to guide their business decisions.

When you present a case that is backed by empirical evidence in the form of data, the argument becomes irrefutable and the decisions become easier. Often, decision-makers will turn to intuition, and while that approach has its own merits, it has been proven time and again through the explosive success and growth of technology companies (who tend to be more data-driven), that leaning on data to validate hypotheses and make decisions based on what the numbers suggest, is a winning strategy.

One of the things we often hear from executives and decision-makers is that they want to adopt a data-driven approach towards decision making, but don’t know where to begin. This is a common problem and one that has a much simpler solution than you think.

You can start by measuring. It is impossible to make data-driven decisions without having actual data. A lot of businesses that have been established for decades are entrenched in legacy systems and technologies that aren’t agile enough to integrate with data pipelines or analytics engines. This puts today’s decision-makers in a conundrum where they don’t have the complete picture and are being forced to make decisions without all the information.

Collecting data can mean a slew of different things based on the company, which vertical they’re in, what tools they use to operate, etc. Through an exercise called Business Process Modeling, the company can identify all the different workflows and data points for the company and determine which ones they are currently measuring, and which ones need to be measured (ideally, you want to measure as much as possible, so that you can be flexible later). If a company does not have in-house analysts or the resources to perform such an activity, business intelligence advisors and consultants like Smart Answers can help with this activity.

Once you start measuring the different metrics in the business process, you will need a place to store this data and a pipeline to ensure the continuous flow of this data to and from the data warehouse.

Data warehouses are large stores of data that are optimized for running analytics and business intelligence algorithms to create visualizations and dashboards for decision-makers who can leverage this information to validate hypotheses and present the data as empirical evidence in making their case.

These days, spinning up a data warehouse in the cloud is extremely easy with services like Amazon Redshift and Google BigQuery to name just a few.

Once the data is securely stored in a data warehouse, data pipeline tools like Stitch Data or Fivetran can be integrated to ensure that all sources of data are aggregated and stored in the data warehouse in a timely manner. These can be run on a timer or triggered by an event to go through all the data sources and look for newly updated data and bring it into the data warehouse for further analysis.

Once you have your data warehouse and data pipeline in place, it’s time to crunch the data and turn it into actionable business intelligence for decision-makers. To do this, you can use tools such as Tableau or Power BI. However, with these tools, you will need an in-house team of technology experts to build dashboards with these tools. And even then, these tools won’t leverage machine learning or AI to deliver any additional information beyond what is present in the data.

This is where business intelligence tools like Smart Answers come in. By leveraging machine learning, Smart Answers delivers predictive analytics and information that the AI can glean from patterns and trends it picks up in the data.

For example, for a mobile/cell service company, a machine learning algorithm can be fed data from the past two years of customer usage trends along with the outcome of whether that customer renewed or canceled their subscription. Based on this information, the machine learning model will be able to connect with the data warehouse and use the existing day-to-day data to identify at-risk customers based on similar patterns and trends found to customers that have canceled in the past. The confidence rating of these predictive models are usually quite high, around ~80%.

Becoming a data-driven company can seem like a daunting task at first, but as you take the first few steps towards it, you will realize that it was a band-aid that you should have pulled off a long time ago, as the gains that are realized from this approach far outweigh the costs. And now with services and tools such as Smart Answers available to do the heavy lifting, there is no reason not to make this the year your company adopts a data-driven decision-making approach.

When you present a case that is backed by empirical evidence in the form of data, the argument becomes irrefutable and the decisions become easier.

Getting Started with Data-Driven Decision Making

The big 3 (Google, Microsoft & Amazon) have all been battling it out when it comes to vying for market share in the incredibly lucrative space of cloud hosting. A July 2018 report studying the market share and growth of these platforms found that while Amazon’s AWS still leads the field with 41.5% market share, Microsoft’s Azure is quickly gaining ground and sits at 29.4%, while Google’s Cloud Platform is 3rd at a paltry 3%. It is no secret that all three companies have devoted an extensive amount of resources towards this sector of their business, and with good reason as Gartner predicts that the Infrastructure-as-a-service market will grow to reach $40.8 billion in 2018.

We have recently launched Smart Answers, an easy to use drag & drop machine learning tool which will sit on top of Amazon. This will allow people who may not necessarily have the technical expertise needed to build intense machine learning models to be able to leverage whatever vast amounts of data they possess and apply powerful machine learning models to gain powerful insight which can generate value in a plethora of different ways.

This news continues a series of acquisitions Microsoft has made in this space as of late, and reiterates the commitment this massive technology giant has made towards strengthening their infrastructure-as-a-service offerings. Google Cloud Platform, although smaller in marketshare compared to its rivals, had a leg-up on both AWS and Azure in this space with their Machine Learning tools such as Tensorflow, which is an industry leader at this point in time.

The onus now shifts to Amazon, who needs to up their game in comparison to the others. While AWS does have slew of Machine Learning tools and integrations available, they have yet to stake their claim with something proprietary or game changing. However, knowing AWS and their dominance in this market, you can rest assured that they are probably working on something as we speak and will continue to innovate as they have done over the last few years, showing us why they are the leaders in this space to begin with.

Regardless of what happens, one thing is for sure — we as application developers end up the winners. For us, these services make incredibly complicated and sophisticated technologies that would otherwise require a team of engineers & data scientists for us to leverage, become easily accessible with just an API integration and some fundamental knowledge. This will enable a new generation of app developers to come up with creative ways to utilize these tools and build remarkable new technology driven products & solutions. We can’t wait to play with Lobe and all the other tools that get introduced in the coming months & years.

In a battle to dominate infrastructure-as-a-service and cloud computing sector, the big 3 (Google, Microsoft & Amazon) devoted an extensive amount of resources towards Machine Learning (ML) tools.

Machine learning takes center stage in battle for cloud dominance

We hear a lot about Machine Learning these days as it has become one of the hottest buzzwords around. However, we are approached by many people who don’t seem to have a clear idea on how it could be applied to add value to their business. Since it is a very broad topic with different types of applications based on the desired goals and objectives, we aim to create a series of blog posts, each targeting a different industry, and just touch on some of the different ways in which Machine Learning can help add value to a business within that particular industry. Today, we’ll start with Ecommerce, as it is one of the low hanging fruits that can quickly be picked for enhancement via Machine Learning.

What kind of Machine Learning is applicable in Ecommerce?

Machine Learning comes in several different forms such as Supervised Learning, Unsupervised Learning, Semi-Supervised Learning and Reinforced Learning. The scope of this blog won’t delve into the technical details, but we’ll just preface our Ecommerce based solutions discussion with the information that while Machine Learning can be used to do things like drive cars, perform facial recognition, or identify a car’s license plate from CCTV footage, what we aim to do in our Ecommerce implementation is to be able to identify user behavior patterns to perform predictive analysis and make proactive decisions driven by data. We’ll explain in further detail.

Perhaps one of the biggest reasons why Amazon was able to demolish all its opponents in the Ecommerce industry is/was largely due to their incredible recommendation engine which is entirely based on Machine Learning. A recommendation engine takes in past user behavior analytics information as training data and learns the different patterns and trends. After ingesting millions upon millions of records of these purchasing habits of users, along with their profiles (Supervised Learning), the system will be able to predict how a new user may end up browsing the website, or what products they may be interested in, based on their browsing habits, the items in the cart, and what people with a similar profile have done in the past. When the user completes their transaction, the recommendation will take into account if its recommendations were successful or not, and further improve its algorithm (Reinforced Learning).

A Recommendation Engine is similar to the rack you find during checkout at the grocery store (usually contains candy, gum, magazines, etc.). It is an opportunity to remind the shopper of an item that they may want/need but had forgotten about, or to entice them to buy something they weren’t even planning on buying in the first place. The more accurate these recommendations are to the shopper’s wants & needs, the more likely they are to purchase additional items.

Another area in which Machine Learning is providing a great deal of assistance to the business team is in Price Optimization. Prices for products can fluctuate a great deal depending on dozens—if not hundreds—of variables. A pricing engine can be created to take into account a great deal of information from the shopper’s profile, current trends, competitor prices, product abandonment rates, and much more, and determine what percentage discount to offer the shopper on a specific product in order to increase the potential of making the sale, while keeping a healthy profit margin on the product. When dealing with a catalog of hundreds of millions of products & variants, with hundreds of factors going into pricing each one of them, you can see how quickly it can become infeasible for humans to do this task with full coverage. By utilizing Machine Learning to optimize pricing on a catalog of products, Ecommerce stores can unlock a massive competitive advantage.

A shopper may not always complete the purchase. They may browse for a while, even add items to their cart, and eventually leave without completing the transaction. Machine Learning can help add value here as well. Retargeting campaigns can be executed to reach out to previous customers who have bought, abandoned a cart, or just browsed your website. These campaigns can be supercharged with Machine Learning by looking at historic data of similar shopper profiles and how they may have been converted in the past via retargeting. In some cases, Facebook or Instagram ads may have worked. In other cases, offering a discount valid for 24 hours on the products in their abandoned cart may have worked. It all depends on what the data is telling you, and that is where Machine Learning shines. As it is already analyzing hundreds of millions of past shoppers’ habits and outcomes, it will be able to predict what is most likely to work when attempting to convert a potential customer with similar habits & profile details.

These are just a few tips and tricks that are being used by Ecommerce giants such as Amazon and Walmart, and more stores are jumping onboard with this line of thinking (for obvious reasons). With the availability of tools such as Google’s AutoML, and similar offerings from Amazon, Microsoft and others, Machine Learning is no longer the scary black box it once was and there has never been a better time to climb onboard the Machine Learning train as it leaves the station. If you are interested in implementing Machine Learning to add value to your business, reach out to us and we’d be happy to discuss the different options that may be available to you.

Learn how machine learning could be applied to add value in ecommerce business and within that particular industry.

How Machine Learning can be used in Ecommerce

Pacing up with the new technologies for your business is always a good idea. Companies who adopt and upgrade their strategies with current and upcoming new technologies in the industry will thrive.

Machine Learning (ML) algorithms are helping companies boost overall sales. This technology is so vast that it can automate and elevate many aspects of sales processes such as product recommendations based on customer personalization, thus helping sales reps in indulging more customers and improving User Experience (UX).

Traditionally sales is more likely to be called interpersonal business, but new technologies such as ML for sales are making organizations and reps rethink their current strategies.

Is using AI and machine learning for your sales a smart move? Well that’s an upgrade on a whole new level!

How AI & Machine Learning Are Helping Sales Teams

Artificial Intelligence (AI) covers multiple technologies and Machine Learning (ML) is one of them. AI-specified technologies can help perform different tasks that are designed to be executed equally as a human or maybe better than them!

Machine Learning pins down patterns solely based on large amounts of data and makes predictions while using those patterns. But it doesn’t stop there – the model will improve these predictions itself gradually by accumulating a growing amount of data over time.

This technology improves with time, usually without human intervention. In contrast to traditional enterprise softwares, where human intervention is vital, these AI models do not necessitate human intervention to improve. Because it evaluates its own efficacy while verifying new data, ML models improve on their own.

This advanced AI-based technology can evaluate consumers and their prospective data, streamline sales prices, identify crucial sales operations, project performance, and estimate which prospects would be the most likely to be expanded. In fact, the list of such applications goes on.

Use Cases for AI & Machine Learning in Sales

AI is improving and revolutionizing almost every facet of the marketing industry. Moreover, many new technologies that are serving companies currently such as chatbots, recommending systems, and virtual assistants are getting magnificent with time.

1. Augmentation of Performance and Effectiveness:

Artificial Intelligence uses advanced ML models by using behavioural data to assess and takes away the plodding work and helps sales reps so that they can focus on high priority work that requires their attention. Like, scheduling meetings, managing calendars as well as assessing teams performance and so much more.

Machine Learning models allow companies to target and pin point relevant customers based solely on their history and data, eventually increasing the chances of permuting a lead.

Lead Scoring is a popular strategy, mostly used by sales reps and marketers to analyze and identify how likely their leads are to buy.

These ML models improve themselves over time by learning about customers’ behavioral data such as their activities on the internet and descriptive data such as their age, hobbies, and other biodata. This helps ML models formulate better personalized experiences for customers.

Companies that are using ML for sales have generally experienced higher footfall of customers than the periods when they were not employing ML entirely. This is because these models help customers have better support and experience – a top priority of every company!

Many of us use recommendation systems without even knowing what they are! Recommendation systems help customers look for the right products, eventually giving them a better user experience. When you are shopping online, ML systems help you come across new products and services.

Usually people tend to engulf themselves while finding the products they want to purchase, which leads to them entirely losing interest in buying those products. Recommendation systems help with this issue in an advanced way. They are like a salesperson who knows what you will like based on your purchase history and descriptive data.

You might have come across Alexa or Siri. Have you noticed how far they have come?

Chatbots and virtual assistants are the future of marketing and sales. These technologies are built upon Natural Language Processing (NLP) and Voice Recognition (VR) which – if oversimplified – is a field of computers associated with processing human language commands directly.

These technologies also continually improve themselves based on AI- and ML- based models.

Will AI and Machine Learning replace the sales representatives?

Wondering if AI or ML will replace you? Your concern, on the other hand, is reasonable!

Although AI is not slowing down, you don’t need to worry. AI’s applications as a technology are not targeted to replace sales reps but to help sales reps boost sales and indulge more customers into business.

Beyond doubt, Artificial Intelligence and Machine Learning will transmute current sales strategies and team cooperation. Yes, AI systems are continually and increasingly taking over tasks that human sales reps are otherwise either unable to do or reluctant to complete. Nevertheless, most AI sales tools give you ascendancy rather than churning you out of the sales game.

Artificial Intelligence and Machine Learning assisted sales will allow salespeople to focus on what they do best. With so many advantages to adopting this technology, salespeople now have a chance to explore advanced technologies along with utilizing tried and tested sales methodologies.

Today, the most successful companies have already begun to embrace and utilize AI.

Learn how Artificial Intelligence (AI) and Machine Learning (ML) assisted sales will allow companies and sales teams to focus on what they do best.

How AI & Machine Learning Implementations Hike Sales? | Smart Answers Insights

How AI & Machine Learning Implementations Hike Sales?

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Getting Started with Data-Driven Decision Making

Machine learning takes center stage in battle for cloud dominance

How Machine Learning can be used in Ecommerce

How AI & Machine Learning Implementations Hike Sales?

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