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Use these 4 types of data analytics to access your small business' analytics performance.
We have moved from an era where the businesses possesses of all the knowledge to today's complex interconnected world where the buyer can acquire more information than the seller and thereby be in control. Thus, to remain competitive companies must incorporate the various social media dimensions and real-time analytics into their distribution, marketing and sales processes if they wish to maximize revenues and customer loyalty while minimizing costs and risks.
Smart visionary companies are well armed for the digital onslaughts. Some are able to change pricing periodically during the day to compete with online competitors that are doing the same. Others have know your customer (KYC), anti-money laundering (AML), credit risk, and/or fraud software as well as upsell features built into their systems so that they can maximize sales while minimizing losses.
This is the brave new world of engaging the customer in the physical and virtual environments simultaneously. It is one of the key ways companies are differentiating themselves.
1. Basic Analytics
To make sense of the vast volumes of data bombarding organizations, companies need systems of insight that can analyze the data.
The primary purposes of these systems are to drive revenues, improve loyalty, upsell, increase productivity, or minimize risk. Everyone does it; just some do it far better than others.
Some companies still do all their analytics on spreadsheets while others have moved from basic historical analyses to predictive analytics. According to some studies, organizations that embrace analytics are more than 2x more likely to outperform peers, grow revenues more rapidly, and increase profits more than 10x.
No matter how one slices it, businesses rely on analytics; but top performing businesses count on it to deliver enhanced results.
2. Historical Analytics
The basic form of analytics usage is historical analysis.
This can be a review of customer buying habits, sales by account type, inventory movements, value of a customer, and other baseline information that can help determine marketing programs, inventory management, sales quotas, etc. These basic metrics that business executives measure are very useful in looking at trends and planning. But they are just the baseline that all successful companies use.
While there are few differentiators in this mix, companies like Wal-Mart use the data as a competitive advantage for reordering and restocking shelves, modifying pricing, and moving inventory amongst and across distribution centers.
Less than 10 percent of all data is ever analyzed; thus, there are numerous ways executives can improve the business through more and better analytics.
The biggest challenges with historical data are its accuracy, consistency and currency. Data ages, which can almost guarantee that the older the data is, the more out-of-date errors there are in it as relates to today's reality.
Consistency relates to the fact that an individual doing historical analysis will likely use multiple files and they may not all be snapshots taken at the same time, causing inconsistencies.
3. Real-time Analytics
More advanced companies add real-time analytics to the mix.
With an effective real-time analytics program that is integrated with online transaction processing, organizations can increase revenues, reduce risks, and be more responsive to customer needs. This approach enables companies to tailor in real-time the generic transaction-processing request into one specifically designed for the customer.
Amazon uses it to recommend upsell purchasing options based on one's history.
Banks use it to determine credit risk or potential fraud and can take action while the customer is still online and before the deal closes. Police use it to determine if there are any outstanding issues or tickets with cars and/or individuals that they have pulled over.
Used effectively executives in most industries should be able to increase revenues (including collections) by up to 10 percent while shrinking their risk exposure.
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4. Predictive Analytics
The most advanced analytics methodology is predictive analytics.
Through the use of predictive analytics executives can stop using gut feelings and base decisions on projected future impacts. For example, some companies use it to determine the discount amount they will offer a customer when on the call.
By anticipating what discount level will entice individual customers, the companies minimize the price cut, increase the probability of a sale, and yield better margins. Companies like Progressive Insurance use the information to determine which individuals they wants to insure and which ones they want to send to competition based upon the expected margins gleaned from past behaviors and known changes in lifestyle habits.
Another use for predictive analytics is to identify in real-time potential equipment failures so that repairs can be made before an outage occurs.
In addition to predictive analytics, businesses are adopting newer techniques such as text analytics (analyzing unstructured text), social media analytics, geospatial analytics (analyzing location-related data), and clickstream analysis (analyzing customer behavior on websites). All are being used to drive business value and are slowly working their way into the operational mainstream.
Summary
Analytics and business intelligence is not new but the way they are being applied today is much more advanced than just a few years ago.
CEOs in mid- to large corporations rate analytics as the top factor contributing to an organization's competitiveness. This change in the executive mindset to incorporate fact-based analysis (versus gut feels) into the decision-making process will make it more difficult for those companies to survive that do not make it integral to their business planning and execution.
Business and IT executives should understand what information they need for business forecasting and planning decisions as well as for the real-time information required to improve margins or revenues, reduce risks, or increase customer loyalty through personalization.
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Competitive Advantage Through Analytics
Finding the Right Data Resource: Asking the Important Questions