BeyondCore analytics software has been used by 21 of the Fortune 100 firms and leading companies across a broad array of industries.
BeyondCore is used by global retailers in HR, operations, logistics, and finance departments.
BeyondCore discovered sales variation patterns, marketing lift for specific campaigns, coupon
effectiveness, ‘perfect store’ incentive effectiveness and product return patterns.
“In less than 5 minutes total, we had been set up on a cloud-based
server, were able to load up our uniquely structured data, analyze it with a single click, and see the results of the automated analysis.” For the full story,
see Why Sears Chose BeyondCore.
BeyondCore has been used to analyze patient length of stay, readmission rates, healthcare
expense, claim rates and underpayment patterns.
30 Million patients, 1 Million patterns, 2 hours:
McKinsey & Co. used BeyondCore to analyze healthcare cost increases for 30
million patients across a million variable combinations. We highlighted the fact that half of
18-35 year old females with diabetic ketoacidosis are readmitted to the hospital.
For such young females, this was not an academic concern, but an immediate possibility.
The readmissions were primarily due to noncompliance with insulin regimens.
The physicians who McKinsey briefed on this pattern were initially surprised, because young
women are usually better at taking their medications. The doctors then immediately recognized
that they had indeed seen such cases, but had not recognized it as an actionable pattern.
After thinking about it, they realized that for young women, not taking insulin might be a
weight loss strategy, an unorthodox way to drop 30 pounds. Our analysis revealed thousands of
such patterns where each pattern is specific and actionable.
Manufacturers use BeyondCore to analyze invoice discrepancy and supplier payment patterns,
key drivers of product returns, as well as customer value and profitability patterns.
Well-meaning dashboard designer hid the key insight:
A leading global manufacturer used BeyondCore software find where and why
there were unspecified product shipment delays. BeyondCore ran the analysis, and the first
insight revealed that a specific shipping depot was performing badly for 2-hour and 4-hour
shipments. When asked why this was the case, the director of these operations said it was
impossible – the depot is in the middle of Texas, and should not have been making any 2-hour
or 4-hour shipments; this depot only shipped to other depots, not directly to customers.
BeyondCore however detected 40,000 such shipments. Further investigation revealed that the
dashboard had been designed a few years back, under the assumption that the depot would never
make 2-hour or 4-hour shipments. The categories were not included in the dashboard, because
they were considered unnecessary. Fast-forward a few years, people and processes have changed,
and people have started shipping directly to customers from this depot. However, because the
dashboard had excluded these shipments that should not happen, the director of operations was
unaware they were occurring. This is a prime example of how preconceived notions/human bias
have the potential to be incredibly detrimental to business operations.
Banking & Finance
BeyondCore has been used to analyze customer lifetime value, customer acquisition strategy,
collections effectiveness, mortgage processing patterns, payment discrepancies and fraud patterns.
Beware what you consider an outlier:
A Fortune 100 Commercial Mortgage Firm asked BeyondCore to determine the
drivers of mortgages that were taking a long time to close. The company already had a model
for predicting mortgage-processing times, but when BeyondCore was brought in, BeyondCore
identified nine specific ways to improve the existing Complexity Score model, and delivered
a 4.7 times improvement in the accuracy of predicting how long the mortgage processing took.
BeyondCore’s software also automatically discovered that the people classified as the best
operators were actually cherry picking the easiest mortgages. Why did this pattern go unnoticed
by the analysts? When the analysts reviewed the data, and saw a few mortgages that took the
operator 180 days to process (but were eventually cancelled), they assumed they were outliers.
They then ran the analysis with the outliers removed, and never picked up on the pattern.
BeyondCore ran the analysis without any pre-conceived notions, and discovered the trend.
BeyondCore could predict, after analyzing all variables, which mortgages were going to be
cancelled after 180 days, and which mortgages would be processed in 30 days. Given that
BeyondCore could predict them fairly well, the unprocessed mortgages were clearly not outliers,
but a key insight.
While BeyondCore has been used in several verticals, the needs of the finance function are
similar across verticals. CFOs and Controllers have used BeyondCore for invoice discrepancy,
days sales outstanding, forecasting, and expense report fraud analysis.
Turning an expense into a source of revenue:
BeyondCore’s software analyzed the invoice data for one of the largest manufacturers of electronics in the world. BeyondCore discovered that whenever a customer had three specific characteristics, there was a large invoice discrepancy in October. When the client looked into this pattern, they realized that these customers were retailers who had a cashflow crunch right before Black Friday. They were really unprofitable in October, but by disputing the invoice, they would get an extra month to pay their invoices. Once the pattern was discovered, the electronics manufacturer could extend the payment date in October in return for a small interest fee, instead of incurring the expense of processing an invoice dispute. However, the company couldn’t go in and make that offer to all of their customers, because then they would not get paid in October at all. BeyondCore’s software allowed the company to microsegment down to this specific small group, and negotiate on an individual basis. Armed with the precise information about which type of customer would dispute the invoice, the client’s Finance team was able to determine exactly which customers needed extra time to pay in October and which did not.