Although big data analytics is one of the most hyped words used in the tech town today, what they are really talking about are more focused elements, like predictive analytics. Predictive analytics, to skip the definition, are those series of neural networks and machine learning algorithms that are always at work resulting in a Cox & Kings offering you that travel package, exactly the way you had envisioned it all year.
The result of years of heightened computational power, strengthened cognitive networks and increased capturing of data from offline and online assets into data analytics systems, have shot the momentum of predictive analytics adoption through the roof.
According to Mike Gualtieri from Forrester Research, statistics show that from the pre-2013 levels of adoption, which hovered around 20%, the growth today has reached “mid- to high-30%” this year.
The power of predictive analytics is already benefiting your business in ways you might not comprehend, but let’s break this down into why it could add further value to your business.
Identifying Unseen Opportunities
Unfortunately, or fortunately, through our human cognition and evolution, we think we can predict where the market or our business is headed, but we don’t end up completely where we envisioned it to reach. As a disclaimer, though, the analysts like Gartner and IDC use both human and analytical intelligence to forecast where industries are headed, and get their predictions quite right. But with vast swathes of data being pored and scanned through by intelligent algorithms across companies and industries, a whole new intelligence domain has been created.And this is significantly due to predictive analytics.
Case in point: Farmers now just don’t depend on intuition and historical practices of cultivation through the year, but are now informed by sensor-equipped weather stations and IoT devices in the vicinity. These devices and systems are constantly receiving and crunching data to suggest when it could be the prime time to sow the next batch of seeds, or when to hold off to let a storm pass.
Making Customer Offers Personalized
Robust customer relationship platforms are taking the lead with centralized brains that combine different departments– sales, marketing and support. Building artificial intelligence and predictive analytics deep into their platforms is something your business could leverage.
You could experiment with stripped down versions based on your budget, but what is key here is to integrate your customer touchpoints and data feeds, for the self-learning models to ingest and suggest tailored offers that your customers just can’t resist. LinkedIn, for example, used data analytics and deep learning in its Sales Navigator to map potential customers from a sales executive’s demographic data.
The technology now makes lead recommendations to help executives to target prospects with a higher chance of conversion, giving them insights into why some prospects could not show interest in LinkedIn products. This is real gold for marketers who usually resort to spray-and-pray tactics.
Flagging Potential Risks
Businesses are always subject to market conditions and live and die by headwinds that could tear the industry apart, or prove to be a windfall and slingshot it to unimagined heights. The risk factor, therefore, is always high.
Thankfully, even though businesses might not have integrated new technologies like predictive analytics into their tech mix, the gamut of services available can save their day, in the event of sudden business or competitor threats. Think of on-demand web analytics, which give new businesses a low entry point of adoption, while giving others add-on options during periods like peak seasons.
Through all of this, the element of risk and potential depreciation of business value is always there. Airlines use analytics and artificial intelligence, with IoT embedded sensors, to sense and predict mechanical failures, helping reduce losses, flight delays and rescheduling. Data scientists from Microsoft use Cortana Intelligence to predict such potential nightmares.
The Art of Predictive Modeling
Datasets against business objectives can be as diverse as chalk and cheese. That’s because elements to track for an e-commerce business might be humungous like demographic or psychographic data. Hence, modelling is the only way to group large elements that share common threads between them, to effect goals or outcomes that are favorable to that target group. Predictive modelling could be self-learning by itself as well, where you could apply predictive analytics, given shifting market and customer scenarios.
Risk modelling, system modelling, funnel modelling and more are what you could customize with ready on-demand web analytics solutions. A credit score is generated based on a condition-based predictive model. With EMC, the data storage vendors predict that there will be digital bits as many as the number of stars in the universe by 2020, you know the path we are on, where data is only going to increase exponentially. Thankfully, predictive modelling will help you manage and still drive sales and revenue growth from it.
You must be wondering how could you move the recommendations you make in your store, to the cloud. Recommendations could be in the form of urging your customers to try out products relevant to their recent purchases, new offers that they could benefit from, or more.
Thankfully, with big data and various logs to trace customer behavior, robust recommendation engines can be built. Add the power of readily available analytics on demand, and you have the chance to cross-sell and up-sell products or services that could augment their main purchase.
Big data is not really about the size, but about regularly bringing new information to prediction and modelling processes. Predictive analytics can now cover gaps where data cannot be sourced, and recommendations can still be made with high chances of conversion. Amazon Prime or even Apple Music use recommendations engines to show new and curated music related to what the user has been watching or listening. You can harness its power in your enterprise as well.
Business Intelligence Imperatives
Combining operational level execution with high-level thinking that usually goes on in the corner offices of your enterprise is where big data and predictive analytics come into their own. Regular reports on actual business performance, changing customer sentiments towards your brand, and competitor or market fluctuations through social and news listening can now be tied to business intelligence imperatives as well.
If strategic level incorporation of analytics is not something you might consider, take the advantage of onboarding on-demand web analytics on ad-hoc basis. Let’s take an example of 58.com, a Yelp-like portal in China. Its executive, Fei Long, at the Predictive Analytics Summit, shared how the portal is using predictive analytics to harness real-time bidding and improve their site’s click-through-rate.
We are already riding this huge wave of robust analytics that are having benefits not just stipulated to sales growth. The beauty of the technology is how it can be universally applied. The market for predictive analytics is expected to reach $12.41 billion by 2022, from $4.56 billion today. This growth rate of 22.1% speaks volumes of how people are seeing its benefits now.
More importantly, however, is the question: Are you awaiting more proof of its potential? With the world awash in data and marketers scrambling to know how to make sense of it, go beyond just making sense to the level where results will persuade you on what’s coming, so you could define your targets for the next fiscal.