Closing the Loop on Marketing Automation

Marketing Automation is starting to come into the mainstream, but many companies are not using the toolset to create amazing interactions with their customers.  I know many brands that have sophisticated toolsets and it is used to show me points and my name.  I get the same exact email that all of their customers get with my name attached, this is not an amazing interaction.

Data and Analytics as the Foundation

Seems logical, right? You would be amazed at how many brands are still working through “We don't know how to get our transactional point-of-sale integrated with our demographic and third-party purchase data.” Solid data management and extract, transform, load processes form the foundation from which a solid enterprise marketing platform is built.

Data is the backbone of any marketing automation solution.  This may be why the technology isn't as pervasive as it should be.  Getting all of the data into one location in a consumable format for marketers is not an easy task.  This is the first step in the marketing automation journey.  Starting with the data will increase the chance to have amazing interactions with your guest.  The more knowledge about the customer, the more customized a communication can be and this is what delights the customer.

Integrate, Orchestrate and Optimize

This is a large category, but an important one as far as customer engagement is concerned.

First up is integration. Integrate marketing programs across channels — leverage insights from outbound marketing programs to better serve customers on inbound channels and vice versa. With consumers switching channels as frequently as they do today, this is imperative.

Marketing automation tools today can currently run many inbound tasks.  This is especially true when sub second response is not necessary.  When giving the customer an option to to click on a button to serve up an offer or promotion, use the marketing automation tool to serve up the offer.  This way it ensures the customer is seeing the same offer they saw in an email you sent yesterday.

Orchestrate campaigns and their offers so that the timing and sequence, as well as the channel delivered, make sense based on individual consumer preferences. 

So many brands are tied to their own timing of communication, not the customers.  For instance, we alway send our bi-weekly communication on every other Tuesday.  This makes it easy for the marketer, however this does not take into account the customer.  

All customers should be on their own timeline.  Marketing automation tools are very sophisticated and can handle this type of philosophy.  Planning the interactions with customers based on their behavior will result in much higher response.  This is the type of delightful interaction customers expect.

Optimization is the final step in the execution phase. Make sure you use analytically based optimization across all channels to avoid over-contact and saturation of consumers. Consumers are only annoyed by receiving an email offer for a product or service that they just signed up for last week during an inbound contact center conversation.

Be sure to optimize constantly.  Marketing automation campaigns are living and breathing entities.  They are never finished and there is always money to be found in optimizing the programs.  Optimization goes much further than over contacting the customer, just as bad is not contacting the customer at the time they want to purchase.  Even worse is offering the customer something they would never be interested in, especially if they have been your customer for an extended period of time.  Tiffany, I already bought my wife the diamond, stop telling me about how amazing it is, you had me at hello.

The last step is to close the loop in order to perform truly integrated marketing. Take the information you learn from the delivery of both inbound and outbound offers: Did a customer open an email, respond to a social message or accept a verbal offer delivered via the contact center? If so, what effect does that have on downstream marketing efforts?

It all comes back to constantly learning.  The more your customer interacts with the brand, the more they tell you about themselves.  I am not the biggest fan of over surveying the customer and when asked why that is, I say its because I survey my customer all the time.  I send them outbound communications with call to actions and if they reply, they are telling me what is more important, voting with their wallet.  If they don't reply, they are telling me they don't appreciate this offer, or maybe it is this time, etc.  Learn from these interactions and enhance your campaigns.

Remember, Data + Insight = Action.  Always be looking for actionable data on your customers and using that in your marketing automation programs.

Data is the First Step to Marketing Automation

I have implemented many marketing automation solutions over the past decade and one of the perplexing findings is how organizations put the cart before the horse when they are installing their solutions.  I like to say marketing automation solutions are "dumb".  Not the kind of dumb as in "this is stupid, why are we implementing these solutions, why not do something else".  They are "dumb" in the essence of they need help from something else to be successful.  They cannot work on their own.

Marketing automation tools are a slave to the underlying data.  All marketing automation tools do is query data and create metadata that is used to create content and messaging for your customers.  Now I am minimizing the importance of the marketing automation tools in that sentence, but from a high level, it works.  

Since the underlying data is what drives the marketing automation tool, that data is the first step in implementing the tool.  Without the proper data, your implantation will fail.  Getting the data into the proper format for consumption from the automation tool is the most important step of marketing automation.  

Understand the problems to be solved

Write out all the different types of campaigns or communications to be run with the automation tool.  This step is vital to understand if there is a gap in your data collection strategy.  Also, this identifies if the data is structured properly to even run these types of campaigns.  This step comes before buying a marketing automation tool.

For example, I want to send a reminder email to all customers who bought a television that specific cables will enhance the performance of their new purchase by 50%.  For this, the data will have to be structured to understand which customer bought a television set, along with cables because you don't want to sound like you don't know your customers, within X amount of time, their email, mailing or app device ID, and the channel they prefer to be communicated with.  Now the data team can make sure they have the proper structure for just this one use case. If the data can't be structured accordingly, then the marketing automation tool will not be able to deliver this campaign.

Define success for the campaigns

This can be a simple sentence in each case.  What this determines is how the analysis of the campaigns performance will be achieved.  Analysis is also part of the marketing automation tool implantation, because I guarantee you that the executives will want to know the impact of this large investment, so the data needs to be prepared to answer these questions.

For example, I want to see the redemption rate and revenue generated, along with the expenses for delivering and cost of goods for the customers who returned to the store and purchased upgraded cables for their televisions.  For this the data will have to meld together the ID for the offer, in this case the cable, along with the purchase item along with the expense data from the marketing automation tool and the sales system.  These tasks aren't easy, but they will pay dividends if this legwork is done upfront.  There is nothing worse than flying blind with your marketing automation..  

 The expectations for campaign execution times

This is one that almost always gets missed.  I have heard of campaigns that run almost all day because the data is not organized in a fashion that is not optimized for the marketers.  That kind of performance may be acceptable if the campaigns are run once a month, but for most businesses that is not the speed of digital marketing.  

For example, I want to be able to run the campaign for the television purchasers every day.  This includes time to run the automation, send out proofs for the collateral and have the deliveries out to the customer by 10AM.  This allows the data team to be able to optimize the data structures to make sure the data can be pulled fast and efficiently for all your automation campaigns.  

This by no means is an exhaustive list, but it is a start to having a successful marketing automation implementation.  No matter how many bells and whistles the marketing automation tools have, if the data does not support the wants and needs of the marketer, it doesn't matter because the tool is "dumb".  It needs the data to perform magic.     

To Benefit From Big Data, Resist The Three False Promises

From Forbes.com:

Gartner recently predicted that “through 2017, 60% of big data projects will fail to go beyond piloting and experimentation and will be abandoned.” This reflects the difficulty of generating value from existing customer, operational and service data, let alone the reams of unstructured internal and external data generated from social media, mobile devices and online activity.

Yet some leading users of big data have managed to create data-driven business models that win in the marketplace. Auto insurer Progressive PGR -1.22%, for instance, uses plug-in devices to track driver behavior. Progressive mines the data to micro-target its customer base and determine pricing in real time. Capital One, the financial services company, relies heavily on advanced analytics to shape its customer risk scoring and loyalty and offer optimization initiatives. It exploits multiple types of customer data, including advanced text and voice analytics.

I believe what most people miss when they hear these success stories is the amount of human capital that gets thrown at these problems.  Hundreds of data scientists create thousands of models, of which very few are actually incorporated into final production.  The reason the Gartner stats ring true is most companies don't have the kind of resources to throw at the problem and most companies won't realize an ROI even if they could throw these types of resources at a problem.

Promise 1: The technology will identify business opportunities all by itself.

This is the direction the technology is moving towards, but it is not there yet.  The technology enables a group of data scientists to identify the opportunities, it's not magic.

Promise 2: Harvesting more data will automatically generate more value. 

The temptation to acquire and mine new data sets has intensified, yet many large organizations are already drowning in data, much of it held in silos where it cannot easily be accessed, organized, linked or interrogated.

More data does not mean better ROI on your initiatives.  In fact, most companies don't take advantage of the data they already have to generate the maximum ROI.  I always use a rule of thumb when purchasing new technology.  If as an organization you don't believe you are already using the technology you currently posses to its fullest, then its not time to move on to something better.  Your current technology should be preventing you from innovating, if its not then you either have the wrong technology or the wrong people.

Promise 3: Good data scientists will find value for you. 

To profit consistently from big data, you need an operating model that deploys advanced analytics in a repeatable manner. And that involves many more people than data scientists.

Remember, data + insight = action.  Actionable data is a combination or art and science.  data scientists provide the science, however you need the team with the business acumen to provide the insight, this is the art.  Data scientists will create a lot of questions that you never thought to ask of your data, but they cannot provide a solution in and of themselves.  

Remember to walk before you run when it comes to data initiatives.  It's always good to have a goal of using "big data" to improve your business and create ROI from where it didn't previously exist, however the journey to "big data" is more important.  These examples of success with "big data" did not happen over night.  They happened because advanced companies were butting up against the limits of their current technology and they were ready to take the next step.  

Using Smartphones and Apps to Enhance Loyalty Programs - NYTimes.com

I am such a big fan of using rewards on a smartphone.  There is no better way to communicate with a customer than with the device they are carrying around in their pocket.  The next evolution for rewards programs is moving from a card in the hand or a punch card mentality to devices that allow even smaller businesses to compete against bigger competitors.  

Smartphones and loyalty apps have begun offering small businesses enhanced program features and automated administration capabilities once affordable only to large companies like airlines and hotel chains. These capabilities also offer the equivalent of a real-world psychology lab for easily evaluating the effects of offerings and incentives on customer loyalty.

The key to any reward program is to capture data about a customers behavior.  If your program isn't allowing you to capture transactional level data in conjunction with the program, there may be a need to consider this approach.  If only to capture the amount spend and the date, this will allow a lot more opportunity for the business.  As I wrote in The True Purpose of a Loyalty Rewards Program, it is imperative to have a program that incentivizes a customer to share their data with you, but not over-incentivize.  The key is to drive behavior by targeting the customer, rather than giving everyone the same rewards.

“Clearly, this is the best of times for loyalty programs,” said Mr. Bolden of the Boston Consulting Group, who recommended that small businesses “focus on the non-earn-and-burn aspects of the program.” He suggested that spas consider a separate waiting room for their app-identified best customers.

“Or when the treatment is over, you hand the customer a glass of Champagne and strawberries,” he added. “If you’re an apparel retailer and you get in a new line from a new designer, invite the top 5 percent of your customers in first so they can see it before anyone else.” The point is that many effective rewards need not cost much to bestow.

Driving behavior is not all about a discount.  Understanding what your customers want and delivering them an experience is more important than a discount.  Because a customer that is coming just for a discount is more than likely not your most loyal customer.

“With apps you now can target specific customers and influence specific behaviors and keep track of all the results and understand the results,” Mr. Smylie said. “Because the check-level detail is now tied to a customer’s profile, we can understand what their purchasing behavior is, what their interests are and cross-reference that against their social media profiles and market to them more effectively and involve them at a deeper level with our brand.”

 

7 Limitations Of Big Data In Marketing Analytics

Anum Basir writes:

As everyone knows, “big data” is all the rage in digital marketing nowadays. Marketing organizations across the globe are trying to find ways to collect and analyze user-level or touchpoint-level data in order to uncover insights about how marketing activity affects consumer purchase decisions and drives loyalty.

In fact, the buzz around big data in marketing has risen to the point where one could easily get the illusion that utilizing user-level data is synonymous with modern marketing.

This is far from the truth. Case in point, Gartner’s hype cycle as of last August placed “big data” for digital marketing near the apex of inflated expectations, about to descend into the trough of disillusionment.

It is important for marketers and marketing analysts to understand that user-level data is not the end-all be-all of marketing: as with any type of data, it is suitable for some applications and analyses but unsuitable for others.

There are a lot of companies looking towards "big data" as their savior, but just aren't ready to implement.  This leads to disenfranchisement towards lower level data.  It reminds me of the early days of Campaign Management (now Marketing Automation) where there were so many failed implementations.  The vendors were too inexperienced to determine how to successfully implement their products, the technology was too nascent and the customers were just not ready culturally to handle the products.  This is "big data" in a nutshell.  

1. User Data Is Fundamentally Biased

The user-level data that marketers have access to is only of individuals who have visited your owned digital properties or viewed your online ads, which is typically not representative of the total target consumer base.

Even within the pool of trackable cookies, the accuracy of the customer journey is dubious: many consumers now operate across devices, and it is impossible to tell for any given touchpoint sequence how fragmented the path actually is. Furthermore, those that operate across multiple devices is likely to be from a different demographic compared to those who only use a single device, and so on.

User-level data is far from being accurate or complete, which means that there is inherent danger in assuming that insights from user-level data applies to your consumer base at large.

I don't necessarily agree with this.  While there are true statements, having some data is better than none.  Would I change my entire digital strategy on incomplete data?  Maybe if the data was very compelling, but this data will lead to testable hypothesis that will lead to better customer experiences.  Never be afraid of not having all the data and never search for all the data, that pearl is not worth the dive.

2. User-Level Execution Only Exists In Select Channels

Certain marketing channels are well suited for applying user-level data: website personalization, email automation, dynamic creatives, and RTB spring to mind.

Very true.  Be careful to apply to the correct channels and don't make assumptions about everyone.  When there is enough data to make a decision, use that data.  If not, use the data you have been working with for all these years, it has worked up till now.

3. User-Level Results Cannot Be Presented Directly

More accurately, it can be presented via a few visualizations such as a flow diagram, but these tend to be incomprehensible to all but domain experts. This means that user-level data needs to be aggregated up to a daily segment-level or property-level at the very least in order for the results to be consumable at large.

Many new segments can come from this rich data and become aggregated.  It is fine to aggregate data for reporting purposes to executives, in fact this is what they want to see.  Every once in awhile throw in a decision tree or a naive bayes output to show there is more analysis being done at a more granular level. 

4. User-Level Algorithms Have Difficulty Answering “Why”

Largely speaking, there are only two ways to analyze user-level data: one is to aggregate it into a “smaller” data set in some way and then apply statistical or heuristic analysis; the other is to analyze the data set directly using algorithmic methods.

Both can result in predictions and recommendations (e.g. move spend from campaign A to B), but algorithmic analyses tend to have difficulty answering “why” questions (e.g. why should we move spend) in a manner comprehensible to the average marketer. Certain types of algorithms such as neural networks are black boxes even to the data scientists who designed it. Which leads to the next limitation:

This is where the "art" comes into play when applying analytics on any dataset.  There are too many unknown variables that go into a purchase decision of a human being to be able to predict with absolute certainty an outcome, so there should never be a decision to move all spending in some direction or change an entire strategy based on any data model.  What should be done is test the new data models against the old way of doing business and see if they perform better.  If they do, great, you have a winner.  If they don't, use that new data to create models that will maybe create better results than the current model.  Marketing tactics and campaigns are living and breathing entities, they need to be cared for and changed constantly.

5. User Data Is Not Suited For Producing Learnings

This will probably strike you as counter-intuitive. Big data = big insights = big learnings, right?

Actionable learnings that require user-level data – for instance, applying a look-alike model to discover previously untapped customer segments – are relatively few and far in between, and require tons of effort to uncover. Boring, ol’ small data remains far more efficient at producing practical real-world learnings that you can apply to execution today.

In some cases yes, but don't discount the learnings that can come from this data.  Running this data through multiple modeling techniques may not lead to production ready models that will impact revenue streams overnight.  These rarely happen and takes many hundreds of data scientists with an accuracy rating of maybe 3% of the models making it into production.  However, running data through data mining techniques can give you unique insights into your data that regular analytics could never produce.  These are true learnings that create testable hypothesis that can be used to enhance the customer experience.

6. User-Level Data Is Subject To More Noise

If you have analyzed regular daily time series data, you know that a single outlier can completely throw off analysis results. The situation is similar with user-level data, but worse.

 This is very true.  There is so much noise in the data, that is why most time spent data modeling involves cleaning of the data.  This noise is why it is so hard to predict anything using this data.  The pearl may not be worth the dive for predictive analytics, but for data mining it is certainly worth the effort.

7. User Data Is Not Easily Accessible Or Transferable

Oh so true.  Take manageable chucks when starting to dive into these user-level data waters. 

This level of data is much harder to work with than traditional data.  In fact, executives usually don't appreciate the time and effort it takes to glean insights from large datasets.  Clear expectations should be set to ensure there are no overinflated expectations at the start of the user-level data journey.  Under promise and over deliver for a successful implementation.  

Busy is not a Strategy

One of my favorite people once taught me the mantra of "Busy is not a Strategy".  So many businesses use the wrong metrics or KPI’s when measuring success of the business.  For brick and mortar companies, their eyeballs tend to deceive them and they use that as their main metric (we were so busy).  For other industries it is market share.  How many widgets can we sell.  The problem can be using the wrong KPI’s along with having the wrong culture can lead to an unprofitable business.

I have implemented the “Busy is not a Strategy” with resounding success before.  We had a casino/hotel in a declining market that had 1,800 rooms.  They were moderately successful considering their location, but they were using the wrong metrics.  Their KPI’s were hotel occupancy and casino revenues.  Now anyone who knows the casino/hotel business is going to ask, what is wrong with those metrics?  They had good casino revenues for the market and an occupancy of 87%.  Most anyone would love these numbers.  Plus, they were really busy.

When we took over the business strategy of the property we saw to get these impressive numbers, there were a lot of giveaways and very low hotel room rates.  To drive the wrong metrics, they were servicing a large number of unprofitable guests.  The belief was if the hotel is full, more profits would eventually flow to the bottomline.  There was just one problem, the other centers of business were not large profit centers and the customers coming in at very low hotel rates did not gamble, because they didn’t have a lot of money.  

To increase profits, we decided we were not going to busy, we would focus our attention on the best customers and try to drive more frequency from these guests while sacrificing the low-end of the business.  This resulted in decreased occupancy and decreased casino revenues.  Uh oh.  Hotel occupancy went down to 44% and casino revenues were down 10%.  The operators were crying “the business is being ruined”.  Even competitors were coming over and asking the operators “are you going to be able to remain open until the end of the year”.  There was pure panic.  That was until the financials came out.  EBITDA was up 100% for the quarter.

By focusing on the best and most profitable customers, this property saw increases where it mattered most, the bottomline.  How did this happen?  The expenses to drive the KPI’s that were important to this property were astronomical.  They were essentially competing for market share instead of profit.  What happened through time, is the best customers started to come more often as that was the new focus of the property.  Casino revenues started to increase through time to levels much higher than before the strategy change, however occupancy remained at 44%.  They did this by focusing on:

• Increase frequency of their top tier from the players club
• Increase hotel room rate
• Target giveaways to the more profitable sector of the database
• Increase customer satisfaction of the best customers

This is very similar to what I see is happening in the phone industry.  There are many manufacturers and most of them are focusing on “Busy” as a strategy.  Now the metrics for busy in this industry are phones sold and market share.  Android accounts for approximately 80% of the worldwide market share for phones sold.  Yet when it comes to profit, that metric is almost reversed.  In fact it is a lot less than 20% in the last quarter.  So how can this be?

The phone manufacturers are selling basically the same thing.  They run Android software that they manipulate in small ways, but all the apps are compatible with their competitors.  This creates an experience that cannot be differentiated in any way but price.  This is the same thing that happened in the PC industry.  All manufacturers ran the same operating system, Windows, and they had to compete on price which forced them to make deals of adding bloatware onto their machines that destroyed the customer experience.  This is where the phone industry is heading.  When price is the main differentiator, businesses eventually will go out of business unless they can outlast the competition.  

So these OEM’s sell many millions of phones to increase market share which leads to…  To what?  I don’t know.  From what I have read these manufacturers have a decent amount of customers that are buying new phones, but they are buying them for the price.  So the manufacturer sold an unprofitable phone so they can gain a customer who will buy another unprofitable phone.  That doesn't sound like a sustainable strategy.  There is nothing that differentiates the experience of the customer enough to make that return customer more profitable.  It is a vicious cycle.  

The only company that is running a different strategy is Apple.  Apple is making almost all of the profit in the phone industry by having a differentiated product that is customer focused.  Apple is doing the same thing in the PC industry, their Macs account for about 10% of the market, but more than 50% of the profits.  Apple has been able to run the “Busy is not a Strategy” strategy to ultimate success.  Sure Apple sells a lot of phones and they would like to sell more, but these sales are the outcome of their strategy, not the focus.  Apple has a culture that is design focused which leads to a product that has a better customer experience.  

Apple is dominating the phone industry because they do not bow down to the marketshare gods.  They focus on the customer first through their design culture.  They make profitable, differentiated products which bring in the majority of the profits in the industry, which then allows them to spend more money on R&D to create more products and services to keep their customers in the ecosystem.  These customers buy new phones at a nice profit which creates a beautiful cycle.  All because Apple is NOT implementing “Busy as a Strategy” 

Are CMOs wasting money on faulty marketing analytics?

Manji Matharu writes:

CMOs are now at a crossroads between data quality and data results. It’s no longer enough to dabble in analytics and come out with the richness required for informed decision-making. The business needs integrated systems across IT infrastructure, and marketers — not IT pros — must champion the call for improved data controls and governance as their cause.

Data quality is the first step in all marketing processes.  Ensuring this is boring and hard, but it is a necessity.  This is the first step when I come into an organization, determine the quality of the data and work to fix that.  Once there is a trusted version of the truth, marketing analytics come to life.  

Data + Insight = Action and Back Again

Adobe Summit brought with it a great nirvana of a near-future where marketers are able to deliver relevant content to customers creating great experiences.  The words that permeated throughout the conference were those, content, experiences and data.  The big stars of the show were content and customer experiences, which I believe are extremely important as I have written before.  

However, there is no right content delivered at the perfect moment to create wonderful customer experiences without data.  Data is the key to making this all work, and not just any data.  No, I'm not talking about "big data", I'm talking about actionable data.  

Most companies are sitting on a treasure trove of data already.  Without purchasing third-party data, understanding every click, customers have data that can transform their business.  The issue is in interpreting the data, making it actionable.  Actionable data isn't a product, it's a culture.  

Actionable data is the combination of art and science.  The path to actionable data isn't necessarily going out and hiring a bunch of talented data scientists, though it doesn't hurt to have these people on your team.  The path to actionable data is marrying the data with the business acumen.  It's not enough to have data telling you something happened, there has to be an understanding of the business as to why it happened.

Once there is an understanding of what happened (science) and why it happened (art), you have actionable data.  Now you can create optimal tactics to deliver relevant content to create targeted experiences in the digital age.  The great thing about this process is it's circular.  Once a company creates great targeted experiences for their customers, customer behaviors will change and the entire process starts all over again.  There are always puzzles to solve and amazing content and experiences to create.  

How to Make Big Data Work for You

This article is the problem with Big Data.  Everyone wants to jump so many steps on their way to true 1-to-1 marketing using data as the cornerstone.  Great marketing is always an evolution.  One step forward using data brings results and different behavior is gleaned from that data.  Then that data is taken and different questions are asked of the data based on the results using the previous data set.  This is how marketing problems are solved using data.

Marketers can't take a dataset that is fairly large, one they are already struggling to make the most of anyway, and then be given a much larger dataset and told to "go make magic".  Marketing with data is a disciplined venture.  As a marketer, make sure you are making the most out of the data you already have before worrying about what keystrokes the customer is making or the "Internet of Things".  

Always make sure the next step in the data is one that will bring you value today.  Have a long term understanding of where the data can take you, but be disciplined in getting there or you just might miss a lot of insight on the way. 

The Case for Why Marketing Should Have Its Own Engineers

Today, he runs the marketing team like an independent agency within the organization complete with its own engineers — a strategy he highly recommends for small teams that need to get a lot done fast.

An interesting article to set up an in-house agency to support all of marketing.  As a database marketer, I truly believe the team needs its own database and its own engineers to maintain this database.  It has to be separate from the IT processes that slow down progress.

Why?

Why shouldn't marketing data be included in the rest of the organizations data?

The simple answer is time.  Most data put into data warehouses are used for analytics.  Sounds just as important right?  Analytics is the driver of making money in the organization correct? 

Sort of.  This data can also include financial data that has different processes based on financial rules, especially for public companies.  Some data might include credit card information, which need to be PII compliant.  This data needs strict data governance and encryption of sensitive data.  All of this takes time.

Time is the enemy of marketing.  The amount of time it takes to get data into a marketing database relates to an amount of revenue that is being lost.  Most data requested into a marketing database is used right away in segmentation for campaigns.  These campaign changes either drive revenue or save on expense.  Having engineers able to get data into the marketing database in an expedited process gives an organization a competitive advantage. The quicker new data equals the more efficient database marketers.  All this leads to more money to the bottomline.  

Seth's Blog: Waiting for all the facts

All the facts are never in.  

The real question isn't whether you have all the facts. The real question is, "do I know enough to make a useful decision?" (and no decision is still a decision).

Seth is brilliant.  There is a quote by Colin Powell

Use the formula P=40 to 70, in which P stands for the probably of success and the numbers indicate the percentage of information acquired."  Part II: "Once the information is in the 40 to 70 range, go with your gut."

You never have all the facts and there is not amount of data that will lead to 100% certainty of making the right choices.  If you wait for 100% of the facts, you waited to long.