Predictive Analytics: Ready to Revolutionize the Retail Industry

Nishant Maliakel Oommen of Cloudnix Software Labs Pvt Ltd talks about the evolution of predictive analytics and how it can be used revolutionize the retail industry.

Emerging digital technologies have contributed multiple smart tools that aid organizations to make strategic decisions. The online retail industry could be the primal benefactor, (when and only used sagaciously). The easy to access and lucrative nature of digital commerce has attracted a great chunk of the populous. The changing market trends are a clear indication of how electronic commerce is going to transform the landscape of marketing in the near future.

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Data-driven cognitive systems and intelligent processes are revolutionizing the world of digital technologies. Every action in the digital spectrum is precisely monitored and is labeled with semantics, giving the action a meaning, and trying to trace the intent. Data when given meaning and compared with multifarious metrics help identify extraordinary marketing opportunities. 

The digital world is exposed to fierce competition. The mere opportunity to tap into diverse clientele has allured almost all brands around the world to hop into the wagon. Few online stores started to give importance to omnichannel marketing. A master plan, finely fabricated to give users the best possible online experience.

Also Read: Why and How Retail Marketers are Riding the AI Wave

Ace with Predictive Analytics - Dip Check on the Future

Currently speaking, predictive analytics is not just reserved as a statisticians tool. The importance has way surpassed its intent and is now transgressed into a productive tool that enables marketers to make real-time decisions. Decisions that could influence the bottom-line. Predictive analytics brings advanced analytical capabilities like machine learning, predictive modeling, statistical analysis, data mining, and real-time scoring.

Predictive analytics collect and analyze the available information to create a model that likely plots the existence of a future occurrence. Predictive analytics has two components, advanced analytics, and decision optimization. With advanced analytics, the system will be able to analyze transactional and historical data, enabling the system to project the future existence of risk and opportunities. The decision optimization help determine which action gives the optimal outcome.
The biggest challenge in retail business is the lack to precisely predict sales opportunities. Once anticipated every business can be well prepared to meet the demand. In the marketing realm, predictive analytics is also tagged as behavior analytics. In behavioral analytics, the business entity keeps track of a user's shopping history, personal data (preferences, interest, hobby, birthday, friends recommended), behavioral signals, search history, etc. Behavioral analytics help create a predictive model that help identify/ anticipate the needs of a prospect. Once a probability is established it opens enormous opportunity for the business to deliver a personalized customer experience.

When to Resonate with your Customers

Feedback forms are widely used to collect information related to customer experience or how a user perceived an engagement with the business. The challenge is that the responses on customer feedback forms are often biased. Most people give a feedback for the sake of keeping the entity happy. On the contrary, they are most likely to give a candid review of digital media and other social platforms. Who wants to review one when they are asked to. 

The business/ marketing world is plagued with competitors who are ready to snatch any possible customer they come across. It gets really tough when corporates with big pockets try to lure people with huge discounts and high volume sales. Complex machine learning algorithms constantly pry upon people, gathering bits and pieces of information, trying to create a virtual avatar. So that the data can be integrated with their marketing efforts. This century is marked by marketing gurus as the age when marketers proactively heed customer requirements and make sure that a personalized campaign is set to run. Ready to capitalize the opportunity.

Use Data to Box, and then Incentivise your Customer

Technology has bestowed us great tools to identify our target audience. Customer oriented campaigns are most likely to give better results. That is one good reason why we do dynamic retargeting campaigns for people who have visited a particular product or landing page. The data allows you to target a particular niche audience that has shown interest towards a certain category of products, or better a  product they have shown interest too.

Hope you have heard of predictive advertising, In predictive advertising, the machine learning system will learn the data and will help gain strategic advantage in creating a better sales campaign. Big-data can closely be tied with artificial intelligence in forecasting the future. Once a smart cognitive system is made to interact with a huge amount of data, it will be able to create and recognize a set of patterns. When the data is structured and the patterns are made visible it gives immense clarity to the marketer.

Traffic to a website can be categorized into cohorts. Each cohort with their own distinct feature. When the traffic is segregated and boxed based on the interest and preferences of the audience, it succors the marketer in creating personalized digital campaigns. The data available can be used to create personas. These personas being the ideal customers, the business entity can target a similar audience using existing data. Most marketing models are created by setting data as the baseline. Data is widely used in marketing to make sure that digital promotions can entice the customer to interact and engage with the virtual entity. That's one good reason why you see incentivized traffic and discounted sales during special occasions. The campaigns are well curated to target and spur the interest of a particular individual.

CRO and Customer Journey Analytics

With increasing digital connectivity (digital touch points) and channel hopping makes it harder for a digital marketer to plot the buyer’s journey. In the current world, the customer journey doesn’t end with a transaction. Most brands are investing huge sums to retain the market share and to keep their customers continuously engaged.

By enabling real-time monitoring, marketers get an opportunity to visualize how people interact with their digital establishment. This gives a tactical advantage to the marketer in understanding what needs to be optimized so that online visitors can seamlessly progress down the buyer's journey. People want their pain points and concerns to be addressed at the earliest. Consider you landed on a product page detailing “Louis Vuitton Alma Bag”, interested with the offer you decide to check out the product. A problem, a technical snag and you are stuck. Perplexed and don’t know how to proceed. Imagine that the support team figures out that you are stuck at the decision stage of the buyer's journey and decides to help you out. How flamboyant isn’t it?

Most business intelligence tools help determine the users intent and help offer a customized offer to the end user. How do you get a cart abandoner to come back and buy your products? Simple, the data enabled business intelligence system will help you identify the cart abandoners and help run personalized campaigns to convince and convert (make use of dynamic pricing) the quality lead/ cart abandoners.

Data to Support your Online Store

If you have dabbled with the concept of digital transformation, then I bet you would have come across the importance of, big data in providing a seamless shopping experience. Businesses that are continuously engaging with the online world needs to place data at its core and should make data-driven decisions. A data-driven strategy helps a retail shop to effectively manage their inventory. With online e-Commerce changing the landscape of traditional marketing, the scope seems wide and promising.

Most entrepreneurs have leveraged the power of e-business to successfully build and nurture their brand presence. Structured data helps you make fact-based decisions. Whereas relying just on intuitions and guesses can cause confusions. Intelligent marketing strategies are framed by online marketers to grab a prospect’s attention. Consider you want to do a multivariate test or an A/B testing on your website, what aids you to make the crucial decision. Yes, the data you gather does.

A great no: of online marketers give importance to reporting. The main reason being that reporting help discovers opportunities and understand what seems working, and what needs to be iterated. With a striking 68% of customers exit a website without a say, Big data can help you recapture those passive leads by synchronizing multiple communication channels and managing those leads prudently.

Reduce Downtime and Cutting Shrinkage

If your online business is crafted around build and ship model then you need to make sure that steady flow of inventory is made possible. Consider that you own an online boutique that sells a wide collection of apparels made out of wool, silk, and fur. Being in a specialized niche you need to make sure that you have the inventory in stock. Or when the time is right you may need to decline the online order with a status “out of stock”.

Business entities have been widely making use of forecasting techniques to identify an increase in demand. The increase in demand means that the business entity needs to be ready to capitalize on the demand.

Similar to predictive analytics the business entity needs to give importance to predictive maintenance. Predictive maintenance help to foresee the likelihood of an unexpected event that could cause a breakdown. It helps to determine the occurrence in advance and make provisions to replace the part/ machinery that seems to meet the set lifetime. Also, one needs to anticipate that not every inventory that comes in will meet the set standards, there exists a possibility that few semi-finished products are damaged or not fit to be used.

Also Read: 6 Ways Big Data helps Companies Mitigate Risks

Parting Notes:- Everything you need to know about your customer is right in front of you. All you need to do is to monitor, analyze, and plot it right. When used wisely, predictive analytics help identifies predefined behavior at an individual level. Behavioral analytics identifies how people will react to multiple online situations and how they interact with diverse touch points. With the customer, at its core, most online stores have shown a higher propensity towards multi-channel experience. Fix customer at its center and using data to give the best possible customers experience.