It is common for us to look back into our past and then form corrective actions to adopt in the present. But that usually isn’t enough to satiate the human mind – concerns about the future usually leave us in a state of constant worry and pressure. And so, throughout history, people have relied on various methods for predicting the future, trying to be as prepared for the future as they can be.  Corporates are no different. Regardless of the business functions (Strategy, Marketing & Sales, Operations, or HR), predictability of the future is one of the most sought out capabilities. Such predictions of ‘probable outcomes/situations in future’ help businesses to be proactive and devise strategic plans to mitigate risks, prevent undesirable outcomes, realize greater sales, improved profits, and deliver greater customer delight. Of course, these predictions need to be highly objective and quantifiable, as they often have immediate implications in the way a business operates in the present.  

So, the obvious question now is ‘How do we make predictions that are objective, with a quantifiable trustworthiness attached to it?’. Predictive Analytics can help businesses with future-state predictions based on past, historic data related to their business use cases.

What is Predictive Analytics?

Predictive Analytics is the application of advanced analytics techniques to predict the outcomes of future events. Data scientists usually build these models by bringing together relevant data engineering practices, statistical models, artificial intelligence, and machine learning including deep learning techniques as required. This model is then run against all available historical data, which forms the foundation for the predictive model. The model then identifies hidden patterns and relationships between attributes in the data, learning to predict future outcomes based on the data. The variability of identified patterns in historic data would help the model to quantify the ‘prediction accuracy’ level. This accuracy level specified by the model could then be used as the benchmark for trustworthiness – but do remember that accuracy could also be an indication of data quality or data insufficiency. 

Predictive Analytics is finding an increased usage among businesses world-over, to utilize ‘big data’ irrespective of it being ‘structured’ or ‘unstructured’, numeric or text, and real-time or non-real-time.  The beauty of the approach is that predictive analytics models are also capable of performing ‘continuous learning’ and improving prediction accuracy by themselves, by making use of incoming, incremental data across time. 

Predictive Analytics – Opportunities to be Proactive

Predictive Analytics can be applied in various industries, across multiple functions of the business, and in numerous use cases. All these applications are opportunities for business teams to be proactive and act in advance to capitalize on opportunities & avoid any undesirable events. 

Here are some of the use cases for predictive analytics: –

1. Predictive Maintenance – One of the ways to improve profits, other than an increase in sales, is by controlling costs efficiently. For organizations that have a large number of machinery operated all-around the year, or are generally heavy on their usage of assets, it becomes highly important to track the efficiency of these assets and prevent equipment downtimes. Using Predictive analytics models, we can now predict the ‘need’ for maintenance, any deviant behavior, part replacement, etc. for every machine across the whole outlay. 

2. Customer Relationship Management – Customer Relationship Management is a critical function for any business, helping with customer retention and customer loyalty, thus making a direct impact on sales, revenue, and profits.  Predictive Analytics has always been finding an ever-increasing adoption in a variety of use cases in CRM, such as targeted marketing, churn prediction, lifetime value analysis, etc.

3. Quality Management  – Quality Management always has the risk of being viewed as a cost, but if you look closely it is a great enabler for better business outcomes. Effective quality management can decrease unnecessary expenses and also increase sales, revenue & profits. Both manufacturing & services sectors would benefit from getting insights on any potential quality issues in advance. Using Predictive Analytics, we can get insights into any threats to quality and thus be more efficient, prevent potential damage to brand value, etc.

4. Fraud Prevention & Risk Management – In Insurance & Banking, fraud in various forms is a concern that has been growing at an alarming pace. On top of this, they face a constant challenge of staying on top of risks related to customer behavior, competition, natural and man-made catastrophes, etc., which has only increased manifold especially in the wake of increased digital adoption, developing markets, and socio-environmental challenges. For all these various scenarios, Predictive Analytics helps to develop a better understanding of potentially hidden threats and opportunities ahead of time.

Of course, none of these are new approaches to data and analytics. Most big brands in the world have been doing this for many years – even for as long as a decade! What is important to realize is that with the advances in analytics and cloud computing, even the ‘kick-starters’ of the world can make use of the opportunities that predictive analytics presents.  

Analytics & Cloud Computing – A Perfect Partnership

Cloud Computing is already more than just a buzzword – most industries have already started reaping the benefits of cloud adoption and today, the technology world sees more and more products & services being built on the foundations of the cloud. Greater processing power and storage capability that can be scaled up and down on a need-basis in the cloud, in contrast with on-premise infrastructure is much more business-friendly. Cloud computing helps industries to work with larger volumes of data in real-time, thus setting the foundation for increased usage of data analytics, especially big data and predictive analytics. 

As per Gartner estimates in June 2020, by 2022 public cloud services will be essential for 90% of data and analytics innovation. Cloud computing can help to overcome constraints related to data volume, speed, etc., and hence can empower industries to reap huge benefits out of their predictive analytics & big data analytics models. Using these cloud computing platforms, data science teams not only develop & deploy custom analytics models in the cloud but also choose from a wide variety of “off-the-shelf” SaaS analytics offerings to suit their business needs.  

The Future

Digital adoption has surpassed the inflection point in a hockey stick growth curve and is growing at an ever-increasing pace. Well for one, if any businesses were hesitant to get their feet wet, COVID-19 has given them the extra boost to jump head-first! 

With all this digital transformation, remember – more data is being generated at every digital touchpoint in every second across the world. As per a Forbes report in 2018, more than 90% of the world’s data has been generated in the last two years. Information is considered to be the most valuable asset and resource by economists. Analytics helps businesses get ahead of their competition, contribute to the growth of the economy and society in general. In short, one can even say Predictive Analytics will empower the world to be better prepared & geared up for forthcoming days. 

The post How Predictive Analytics Can Help Businesses appeared first on Black Rock IT Solutions – Software Product Engineering Services.

It is obvious that the better the customer feels about the product, the better the sales.  A delighted customer usually becomes an evangelist for the product and thus, an extended member of the sales team. To ensure a delightful customer experience, the product team should know the customer – his motivations, his pain points, his aspirations. 

Product Analytics can help them figure this out.

What is Product Analytics?

Data about customers are everywhere, and collecting this data is the first step in the process of understanding it. Once data is collected, the necessary steps to obtain meaningful insights from these data points need to be carried out – these become valuable pointers on the table during product strategy discussion. The whole process of ‘understanding the customer’, starting from data collection to generation & presentation of actionable insights for decision making is called Product Analytics.

Product Analytics is especially crucial for technology products, as customer footprints are placed digitally here and can be accurately tracked to better the performance of the product and make it better suited to the customer’s expectations.   It also enables automated classification/segmentation of users across their life cycle based on multiple parameters and factors. The data generated would also be beneficial to understand customer churn and probable reasons for it. Such analysis would also pave the way to predictive insights which can point out customers who are probable to churn in the future.  Analytical insights on customer journeys across various ‘funnels’ would also help to identify pitfalls and avenues for improvements in user interface and user experience offered by product websites/apps. 

Product Analytics must be adopted as a vision by the product team and should be driven as a holistic operation that encompasses continuous data collection from multiple sources (about customers), continuous integration of such data into a consistent single source of truth as a historical data warehouse, algorithms to discover meaningful insights from the data and presentation of the insights in self-intuitive visual formats via dashboards & reports, back to the product team. Strategies developed based on customer understanding can help to meet customer needs (even latent needs), improve customer experience, and thus create a delighted customer.

Why Product Analytics?

We live in an era that is becoming increasingly digital every day. The digital transformation wave has reduced switching costs for customers in almost all sectors & segments of the business.  Customers are empowered with capabilities to gain more product information, make faster buying decisions, and make their opinions on product websites/apps known to the public domain within no time. 

Thus, marketing strategy and advertisements for the product must be crisp and targeted segment-wise. It helps to increase the quality of incoming leads and conversion rates, and decrease marketing expenses as well. It is also crucial for organizations to be proactive & have systems in place to ensure customer retention and increase sales by upselling/cross-selling.

With the volume, variety, and velocity of incoming data increasing day by day, any organization would require an automated, tightly coupled, robust Product Analytics system in place that accepts data from multiple sources, processes it, and provides actionable insights, all in real-time. It enables the product teams to understand what the users do in real-time rather than assuming these actions from gut intuitions. According to a Mckinsey report,

“Companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment. For example, companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not.”

Who should adopt Product Analytics & when?

Product Analytics is a vision that needs to be adopted as a culture at any product company, irrespective of their size and volume of business.  Anytime is the right time to start with product analytics and it has to be considered a continuous exercise rather than a single-time activity. Digital transformation is bound to bring on more innovative changes to the business world, as it does today, across sectors like banking, retail, healthcare, education, travel & logistics, agriculture, etc. Data will always be available in plenty and competitive advantages will continue to be built around customer data.

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