3 roi killers for data projects

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3 UNUSUAL ROI KILLERS FOR DATA PROJECTS

Bob Suh, CEO and Founder

All rights reserved. 2®

Here are 3 reasons to be skeptical of your returns

on data

3


If you miss1, it

takes too long to reset

1

1. Missing could be: 1) being wrong about how users will react to data, 2) being wrong about which users to target, 3) predicting events that don’t occur.

2. It takes about 20 seconds to reload a musket. A problem if the enemy is less than a 20 second run from the shooter.


Because we spend too much time processing data, believing more is better

Ready Aim Fire Check Shot Repeat

Capi

tal a

nd T

ime

Engagement Rate

BIG DATA

Process all data

Cycle time to develop and deploy

Predictions off and engagement rate low


And we’ve made it too complicated to make changes once we know we’re wrong


And sometimes we don’t even know we missed until it’s too late


People change their behavior

when they know there’s a

downside to being tracked

2


Because sales people keep two records of their activity: unreported and reported

6%

ONCORPS CASE

Unreported

opportunities

Reported

opportunities

6%6%

Salesforce research

on conversion rates of

all CRM opportunities

90%

Actual conversion rate


And this results in a common CFO revenue forecasting practice known as “the haircut”

ONCORPS CASE:

CFO of a major bank trims 20% off the top of his CRM forecasts


Predictive systems will be

wrong more than they’re right, making users ignore them

3

11®

Because it is mathematically impossible to accurately predict something rare


Prior Rate of Occurrence

The odds something actually happens

Reporting Accuracy

The odds you called it right when it happened

5/50 = 10%

Red = +

Red = -

Red = +

Not red = +

Not red = +

+ = 80%

FalsePositives

The odds you will be wrong when you predict it\

1

70%

+ + +

+

+ +

+ ++ +

\1. Applying Bayes Theorem of conditional probability

1 2 3


And users stop responding when data are inaccurate and negative

Predictive alert

Didn’t happen

Response declines

Data distorte

d

FALSE-POSITIVE CYCLE


So how can you change your data projects to avoid these ROI killers?

?


Make it easier to measure reactions and adapt to changing decisions

Ready Aim Fire Check Shot Repeat

Capi

tal a

nd T

ime

Engagement Rate

BIG DATA

Process all data

Cycle time to develop and deploy

Predictions off and engagement rate low

ADAPTIVE DECISION ANALYTICSLearn from select data

Decision makers track goals, choices and outcomes

Nudge with personalized data to improve decisions


Make the user experience a self-regulated experience with no downsides to sharing data

Adaptive decision analytics Is likeToday’s user experience is like


Learn from the lessons of sports analytics in managing to only the most predictable outcomes

1st

2nd

3rd

HR

Players are 1000% more likely to get to the first stage then the final stage…

32%

4.9%

.05%

2.9%

…teams that focused on these odds were significantly more

efficient

The top 10% of teams at achieving the first stage paid $530K per win

The bottom 10% of teams paid 43% more - $756K per win

Players who perform better at the first stage have significantly lower market values

The odds of getting to each base from a single attempt in

2015

21


And follow a method that allows for quick changes to hypotheses based on user reactions

1

Loading…

What data do we need to evaluate our goal?

2

25%

45%

17%

100%

39%

What are the odds

we’ll meet our goal?

3

+

What scenarios

may change our odds?

4

Are the right people

reacting to our nudges?

5

How may we adapt based

on the response?

18®

©

Cambridge, Massachusetts | Bristol, United Kingdom


THANK YOU

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See our video here

http://www.oncorps.io/

https://www.youtube.com/watch?v=yEVeJXslI74?rel=0

3 roi killers for data projects

Data & Analytics