There are so many data providers and they’re examined and chosen by files, size of company providing, white papers not segments and results this is a big problem and just one of many.

When I started in the data business we made the mistake of using licensed data from all 3 credit bureaus, 4 county record providers, at least 4 demo files, 5 transactional files and even more…What we learned after much testing is all the providers had value but in certain elements not the entire files. We paid for so much redundant data to find the right elements to build our data products.

These problems created an entire business unit, we were very lucky to be serving the financial markets which paid top dollar for modeled data products and allowed us to still turn profits after paying all the license fees but after years of building our own products from these files we found in each file components of what we needed and cut out the rest of the files.

The process was intense and needed a lot staff heavily educated and experienced in modeling and data science to build products to test and then cut the necessary elements and coverage. The amount of variables were insane from geographical coverage, criteria validity, hygiene, data freshness, audience reach and match rates. We also needed to include the sales team to ensure we were building something based upon real client feedback and demand.

The next step was my partner and I renegotiating terms with our data providers from full licensing agreements to partial segment agreements…only paying fees for the data segments needed to create our unique products. We had to prove there was more financial upside to on demand and segment licenses versus full file installs. Not an easy sell considering the ease and profit of dropping off a full file every week, month or quarter and having us do all the hygiene and dedupping. We learned omitting data was just as important if not more than allowing data.

We negotiated based upon update cycles, batches of elements, higher prices for more utilized data sets and selects, and even longer term agreements if we had to, we knew this was risky but we saw the reward and huge unique selling proposition for our company. Some vendors laughed, some played ball, some took advantage (or so they thought) with higher pricing…our key to the pitch was our training of our sales reps to sell data the same way we needed to buy it and would build it was going to make us all more money. We were going to productize data in a way never done before.

utilization

There is so much more data now traditional, cookies, ip, set top box, survey, network and social but I see no difference in how it is needed to be purchased and sold. However the difference I see is now it’s my job to get third party companies to follow a business and sales model we created versus my own employees. I need their trust that it will work and their clients will see better performance.

I look at the adtech and martech space and still see these remnant buys with some audience data packaged but not enough lift in pricing and audiences that are still way too large to serve too…so too much data and not enough emphasis on a sales team that understands and can pitch the value in data elements being muti-compiled to create smaller audiences to create higher returns for the providers, the servers and the end user all simultaneously and this is possible…it’s being done but not enough.

Why?

The data providers are not offering the two key services needed to make this harmonious success theory a reality…training and support. Constant training and support.

Here is an example of the inability for me to buy data from a third party:

I logged into a native ads platform I know pretty well to launch a B2B campaign for a client of mine, simple steps went smooth, loaded the advertorial, pictures, URL etc. Below was the select audience data immediately I selected a source I am very familiar with I have worked with their data for years. So I began with 3 selects and pressed NEXT, the spinner started and just kept spinning until it crashed…OK mistakes happen so I started it all again and it crashed again. I ended my attempts to run the campaign the third party and the source lost utilization from me.

The data was not installed properly, the match process was not reaching out and grabbing right and the MVC was not stable so no sales and no campaign for my client…everyone loses.

The best examples to this training model I can give are Oracle and Salesforce, two companies that created products that created huge value for them and their clients by having powerful consulting businesses constantly providing training on use of the products and the data the products provided.

So now these two giants are buying the data companies and slowly attempting integration into their existing software products while also creating separate data sales business. The wall they are hitting is lack of utilization and ability to sell premium data products. The data is being utilized by companies trying to increase performance but not the price for that performance. This makes 0 sense to me, quite simply you buy a Porsche Carrera at one price and a Turbo at a much higher price…the turbo performs better and offers more…and it’s evident.

Building better performing data products and audiences if done right should be evident right down to the bottom line of both the provider and the end user, which means both parties should increase price for value.

So how do we change what needs to be changed to make this happen? Same way Oracle and Salesforce became multi billion dollar companies, provide the product and train the end user on how to best utilize it and sell it. Sales training by the vendor an old software model that needs to be packaged and delivered properly for data.

I remember years ago at Red Clay when the reverse mortgage business started booming and we were crushing direct mail response rates for a solid year, then those response rates started declining fast. We needed to change the audience. So we took in a ton of campaign data the positive data and the new negative data and looked at the changes…the ah ha moment came when we needed consumers with more debt management issues. So we had to use one of our vendors that had more late payment coverage. We immediately switched the late payment criteria from one bureau to another and instead of 60 day lates we went with 90 to 120 day lates…BOOM response rates went back up in tests.

Now we had built a new and improved data product for reverse mortgage that would double our competition’s response rates, next was a series of sales meetings that included all staff involved with product creation to create new pitches that documented how we built the file and the new and higher price we had to demand for it because with these response rates clients would double their profits.

This example is so agnostic to any data product/service, any audience, any use of data from traditional to digital to research and planning utilization. But it takes this holistic approach of all the teams working together, collaborating and being intimately involved from problem to solution to really embrace, trust, get excited about and sell the product. This requires training!

 

Success is Freedom, But it’s not Free

Posted: December 28, 2015 in Posts

The modern business world is now focused on creating entrepreneurial environments in corporations while turning away from the more traditional company/employee model. One explanation for this change comes from the startup world’s model for growth and the creation of a “startup culture.” This culture forms the foundation for success.

And that foundation is built upon an environment where employees feel that they are true stakeholders in their company. They must share in larger positive outcomes and be recognized for their versatility in pursuit of shared companywide goals. In other words, they can readily assist various teams by employing their core skill sets and lend expertise in other areas that they are not necessarily “specialists” in.

Along those lines, the “art of a startup” relates to the structure of roles and delegation of duties.  The combination of unorganized duties (meaning impromptu multi-tasking from each member to assist others) and organized duties enable companies to reach common goals and necessary milestones.  The “startup model” requires the time, energy, equity, selflessness and humbleness of each participant from CEO to intern! It’s not free.

It’s become increasingly more popular for executives to leave the Fortune 100 world to launch a startup and explore dynamic, new, and innovative business models. These business models, according to many executives, would have helped them build divisions, create teams and increase growth for their former employers. However, they were unable to develop and implement these more contemporary goals based upon the confines of the traditional “corporate” structure.

After shifting to a startup based approach, these former executives begin assembling new teams and start relying on their team members to break free of the “corporate” shell.

Coming out of that shell requires fast decisions and openness about the challenges they will face. Practically speaking, the “startup culture” leads to working non-traditional hours and sitting in the same room as everyone else at work. The traditional walls and closed door office setting of the past is shed for a more conclusive, open, and team oriented approach. In other words, the days of being afraid to knock on the boss’s door are gone in startup land.

The open office space layout creates a free environment and enables leadership to work with their team on a daily basis. Executives can spend each day with their employees, working just as hard and fast as they are…and as late.

That’s not to say that there are some benefits and lessons that startups can learn from corporate America. The new team members of a startup formed by a former Fortune 100 executive would be wise to adopt certain business aspects and concepts from a CEO’s prior experience. Certain elements of the Fortune 100 world can be seamlessly integrated to a startup creating a synergy and a hybrid business model. The startup success ratio, unlike the proven but potentially outdated corporate model, is still quite low; everyone is working against the odds which engenders an expensive risk.

It’s important to keep in mind that a former Fortune 100 executive who enters the startup space chose to eschew  a comfortable salary, a pension, stock options, excellent health coverage, paid vacation (real vacations, not working vacations) and the benefit of knowing that there was a safety net of hundreds if not thousands of people working to ensure consistent revenue and profit. These factors establish a certain degree of comfort, and conferred executives with the confidence that stems from knowing their income was safe, they would receive bonuses, and they could strive for promotions that were always just around the corner.

Unlike the corporate world, startup hires are often coming on board with student loan debt and expectations of lower salaries. If they believe in their leader and the business, many are willing to take on equity as opposed to a high salary. They expect to oftentimes work a 7 day work week and realize that 9 to 5 days are gone. Their skin is in the game, so to speak, and they are making a riskier choice to forgo a Fortune 100 company in search of larger goals and the potential for great success.

Most startup employees want to leave their mark and create an impact in their field. They consider the possibility of working closely with a founder to be a true benefit. Sitting in a cubicle and subsisting three levels of management away from the person they actually work for is unpleasant and diametrically opposed to the startup ethos.

As a result, everyone from the executive starting a new business to new hires shares a certain degree of risk as well as the potential for a great reward when launching a startup. All parties entering the startup arena make an expensive decision that requires great commitment, transparency and honesty. The allure of the startup, however, is the freedom to express your ideas, create without boundaries, and accomplish individual as well as team success…ah, freedom.

Yes, in the world of entrepreneurs, employees may have to work much harder than they would at a large corporation, putting in more time, working on multiple projects at once, and quickly pivoting to many new tasks that may be outside their experience or comfort zone.

It is this freedom that shapes the allure of a startup: the ability to be a part of a business’ ground floor, to create success from the bottom up and to be able to look back and remember when it was a few people; a few desks; and some seed capital that enabled you stand by your CEO’s side while he or she rings a stock exchange bell!

This freedom and this success comes with great risk for all parties involved and necessitates many sacrifices, sweat and hard work…it’s not FREE. Actually, it’s quite expensive! But with great work comes great rewards. Merging the positive attributes of corporate America with the benefits of a startup is a great way to ensure that a startup generates revenue, creates great products, and achieves success.

 

Published On: Blueliner Marketing Blog

The evolution of data for me has been like watching my child grow up and use different forms of communication abilities to tell me what my child wants and what my child needs. In the marketing world we can call these luxuries versus necessities or other jargon is always available as we know. After over 2 decades of being in the data business from traditional lists and list management, email, cookies, credit data, IP addresses and more.

I have had to learn to evolve with the data and find unique ways in listening to it, to create product and action. I formed my own exercises over the years to better use analytics to hear the demand of the consumers and deliver the relevant experience, services and products to them based upon that demand.

Demand is in data and data delivers the demand we need to see, you’re out there searching for what an audience wants via 1st to third party data partnerships and audience product development…creating data products based upon signals of demand.

The stages of child communication development I find so similar to the stages of data communication development over the years as we have been able to access not only more data AKA Big Data but more relevant data that is now closer tied to a consumer versus a cookie or a model of a consumer…now being “coined” people data.

For many years, I would say majority for me data we have taken in and modeled is always in the arrears, even while managing a DMP that we had licenses to 7 data providers including the credit bureaus the best and fastest signal of an event we could receive was a soft credit pull trigger updated 24 hrs to 1 week and this data was super expensive as well could only be purchased and utilized by FCRA approved marketers, such as Automotive, Mortgage, Credit Cards and approved lending practices…so the fastest data was limited to verticals and laws.
So let’s get back to the communications development of the data child:
(just some bullets not too deep)

0–6 Months Old

  • Watch your face when you talk to them.
  • Recognize your voice.
  • Smile and laugh when other people smile and laugh.
  • Make noises, like coos or squeals, to get your attention.
  • Have different cries for different needs. For example one cry for hunger, another when they are tired.

6–12 Month Old

  • Listen carefully, and turn to someone talking on the other side of the room.
  • Babble strings of sounds, like ‘no-no’ and ‘go-go’.
  • Make noises, point and look at you to get your attention.
  • Enjoy action songs and rhymes and get excited when sung to
  • Take turns in conversations, babbling back to an adult.

So here we see at early stages what I would call signals and as parents these signals are what is needed for a parent to learn to communicate with and teach communication to their child. Listening to these signals, reacting to them and developing your child from them can dictate the future of your child’s overall communication skills as they grow into an adult. All of these signals are the same signals data scientists need to listen to and adapt to from consumers and the data we see as they search, research, consume, click and transact.
So let’s jump ahead a few years…

2–3 Years Old

  • Listen to and remember simple stories with pictures.
  • Understand longer instructions, such as ‘make teddy jump’ or ‘where’s mummy’s coat?’
  • Understand simple ‘who’, ‘what’ and ‘where’ questions.
  • Use up to 300 words.
  • Put 4 or 5 words together to make short sentences, such as ‘want more juice’ or ‘he took my ball’.
  • Ask lots of questions. They will want to find out the name of things and learn new words.
  • Use action words as well as nouns, such as ‘run’ and ‘fall’.
  • Often have problems saying more difficult sounds like sh, ch, th and r. However, people that know them can mostly understand them.
  • Now play more with other children and share things.
  • Sometimes sound as if they are stammering or stuttering. They are usually trying to share their ideas before their language skills are ready.

5–7 Years Old

  • Focus on one thing for longer without being reminded.
  • Rely less on pictures and objects to learn new words.
  • Learn that the same word can mean two things, such as ‘orange’ the fruit and ‘orange’ the colour.
  • Learn that different words can mean the same thing such as ’minus’ and ‘take away’.
  • Understand feelings and descriptive words like ‘carefully’, ‘slowly’ or ‘clever’.
  • Use language for different purposes such as asking questions or persuading.
  • Share and discuss more complex ideas.
  • Use language in a range of social situations.

The development from 2 to 7 years old is a whole bunch of data we need to analyze, react to and derive relevance from to use for Audience Science, Marketing and Product Development. Sharing is a main bullet in both age groups and of course something parents strive to teach their children…we live in the social sharing world now and with proper data can react and market to consumers with true relevancy to their demand based upon their sharing habits.

The other key takeaways from these age groups are linguistics, research (questions and answers) the basics who, what and WHY. The development of ideas and use of language to communicate those ideas, we can not speak to all consumers with the same message, content needs to match the segmented audiences and the message has to be personalized!What I find really interesting and important starting with the 2–3 year old segment and their use of up to 300 words and then in the 5–7 year old segment relying less on pictures and objects to learn. The iteration of that content by creatives and copywriters collaborating is key…how to best separate and combine contextual messaging and image based messaging to deliver relevant and personal marketing creative.

We can look at other age segments but science tells us the segments above lead to the life of a child, they are the most impactful and memorable, they can define so much of the child’s future behavior socially, the relationship with the child’s parents, the interaction with peers through schooling and the proper communication to others a child’s needs.

However this also illustrates the data will be fuzzy and our skills are needed in analyzing the research, the data in, the consumption of content to derive a clear understanding of what a person is in the market for and how they should be marketed to.

Wow how my child named Data has gown up…my early years of licensing data ranged from real estate data, transaction purchase data to demographic and lifestyle files at best we received 30 day installs. Yes we archived files to do look backs on consumers to build data products that were able to find life events based on over 150 elements that showed a propensity of why a transaction, a change, a move etc happened but it was all built on probabilistic models that at best hit 60% accuracy and that was considered AWESOME!

Building FICO score models was not only the beginning of my small data shop in 1998 becoming a full service data marketing agency but also what took my career from a data marketer to an analytics product developer and these score models at best were a 50 point delta but at that time was AWESOME and provided a product for non FCRA approved marketers. This was the beginning of something that never ended, keeps evolving and what I work on everyday…creating audience based products, enhancing products with audience analytics, developing new products as well as decisioning services used in industries ranging from entertainment, private equity, direct marketing and so much more.

We now live in an age of the ability to predict in real time versus using just historical data modeling that has update restrictions because it couldn’t move as fast or at such scale that the web can move data now.

One thing I didn’t touch on yet is that ALL parents deal with or have dealt with children’s Temper Tantrums for many reason but mainly from frustration at an early age that their point/message is not clear and they don’t know how to make it clear they feel they are doing their best with the communication skills they have.

Well bad, irrelevant, horribly timed, poorly placed, intrusive, non-personal creative and messaging as well as evidence that no data was taken in to deliver the right message, the right way at the right time to be personal and engaging will cause all marketing segments i.e. people to have Temper Tantrums.

I know I have tantrums daily because of these issues!

 

 

 

DataBlogImage2

I am data. You are data. We are all data…and that’s why we have to zone in on our processes of data analytics and science as people because that is what the data is, people.

I am not alone here. I have had conversations with many of my peers and competitors who are all fully aware of how we must take an emotional approach to our work. This is because there are emotions and at times, very important life events that we are affecting through enablement and change, hopefully with the best of intentions.

What college should I attend and why?
Predicting intent to move, the changing of home caretakers, purchasing expensive items that will have a big impact budgets, choosing your next job – all these decisions affect so much more than a click or a day of someone’s life. We are mapping the flow of this traffic, its direction and the related content. Our research data, when brought to action, becomes the behavioral data about the people who make these types of decisions.

I feel the pressure of the people, not the CPM! 

Marketing, advertising and research through audience science, development, management and optimization, are all very important. However, without an emotional understanding of the people and the goals, we can easily fail.

Even understanding and creating the right content might not be enough to accomplish goals focused on the people – not just a CPM or CPC. The IP models get loaded into the DSPs for programmatic media buys and we can often forget they are actually people. We can no longer run those buys the same way we have watched display advertising become a commoditized market of price and reach, not quality and relevancy. We need to bring favorable and tolerable marketing for the people!

But here is why advertising has the opportunity to really harness the power of the people – by really listening to the that data, analyzing it, finding stages in the buyer’s journey and making it speak to us. However, if you don’t engage with the proper content that says “I hear what you want and we are giving it to you through incentives that increase our life time value through a data/people relationship”, then we are not committed to a greater outcome and relationship.

We can bring the horse to the water from audience analytics but will they have a drink?

We need to emphasize:

  • Creative solutions
  • Relevant content
  • Offers and meaningful CTAs
  • Lead and prospect nurturing through real-world CRM solutions

The ‘Relationship’

It must be a fair relationship between your data partners, ad agencies and company processes. This how we get to a win-win situation, for both the business side and the consumer satisfaction side. This is also why it never matters who the smartest guy in the room is it’s the contribution of each individual – their skills and experience independently based upon the goals we are all there together to achieve. The lions need to form Voltron.

As for us data, audience, people profiling folks – we can help by doing our part. However, it’s rare to be able to have an audience scientist who is skilled in UI, UX, design and content. This is an illusive mash up and usually is only found in the old school direct marketers that have evolved with data for over 15 years plus of experience. This is because back then, it had to be done this way. The creative in DM/DR was responsible for processing the data and then communicating the meaning of these analytics back to the creative – much more than A/B testing.

Insights are only relevant in advertising and marketing if action can be tied to them. Data can be extrapolated from most of the insights, especially from research data and not old survey panels. Keep in mind, I don’t just mean for media planning and buying. This applies to making actionable decisions based on locations, banners, landing page optimization, customer programs, sales times, seasonality, AdWords, competitive analysis, consumer experience and much more.

Intent, from cookies to IP addresses, started and has been evolving since about 2012.

Nowadays, the cookie is basically irrelevant in intent modeling and audience development for so many reasons, mobile being of course the big one. There are no cookies in this terrain and all around the cookie…well it’s just not clean data in! So now, we need to uncover real predictive intent and this comes from research analytics and modeling that can be tied to indexing page consumption at the UA/Device level. Actions can then be taken from all aspects of communications and engagement, creating many important triggers.

Data is becoming more and more useful for practices such as Venture/Private Equity, PR, Product Development, Market Analysis (sizing, opportunity, competition, etc.). I cannot stress enough how we can no longer just load IP addresses into a DSP and start programmatic buying with old methods. While this is an important use, it needs proper recency and frequency models attached to it for optimal performance, in brand as well as direct. Many are busy in their labs as we feel, with so much data these days, there is limitless work to be done.

The bottom line

While it’s really fun to see what comes out of an audience profile in the early part of the build and then, how many segmentation and utilization doors get opened, I often find that it’s very easy to get lost in the data.

So how do we maintain a clear focus?

First off, if we are tasked with a project that has a defined end result, this helps a great deal. Additionally, being focused on improving campaigns, strategies and ideas all of which are based on the who, the what, the where and the when, provides you with a clear road map to follow.

With so much research and work being done to avoid digital ads all together for consumers, it’s clear we’re not performing optimally.

As Marketers, we have the power to do better! And therefore…we must accept the responsibility.

Originally written & published @bluelinerny

1st Party Data – The Key to Opportunity Is Already In Your “Hands”

 

You may not realize it, but you may be growing your business by harnessing the power of the data that you already have in your CRM.  This is what we call 1st Party Data.

While we hear a lot about 2nd and 3rd party data nowadays, what about 1st party data? What is it? Well, it’s the data that belongs to you. The more of it you have –  and the more you know about each individual – the more powerful the rest of the process will be.

You can utilize your preexisting 1st party data even more effectively from referrals, upsells, and new customers to increase your profits. Here are some helpful terms to keep in mind:

1)    Onboarding – a new term that refers to exporting your data and then importing it into a data management platform that can model your best customers, enabling you to learn more about them by appending 2nd and 3rd party data.

2) Look-A-Likes – this means using data modeling to create the closest, unique look-a-likes of your best customers…the top 20%! This is a sensitive process for many reasons. First, it is paramount to target and then omit the data that’s not relevant to your goals or business services/products. Second, you must know what data to retrieve.  Third, locating sources to retrieve that data is essential. Web behavior, demographics, lifestyle, and household and transactional data that is appended to your file will enable you to determine consumer intent, demand, desire and the reasons consumers share positive reviews.  As a benefit, this process will also directly and indirectly refer you new customers.

3) Actionable Data – that’s what look-a-likes and onboarding create when you’re 1st party data is in the right hands, with the right minds and aligned with the right tools.

The goal is always twofold: to know more about your customer than you could have ever imagined and to use your  data effectively.

Whether you’re attempting to harness 1st party data, 2nd party data, or 3rd party data, the goal is always business growth. Here are some great ways to achieve that growth:

1. Referral Program – This straightforward method can start by creating one-off incentives and to see what quick and varied results that fluctuate in relation to different types of customers.

2. Upsell/Cross-Sell – Finding the correlations between customer data and the other products/services your company provides (or other products/services that your company can partner with other companies to provide).  This improves your customers’ access to your products/services.

3. Prospecting – what methods are working the best: PPC, SEO, Email, Mobile, Direct Mail, TV, Radio, etc all need to be examined.

4. Mobile  – I will not put stats here; we all know the smartphone has changed how we communicate with and market to our customers. The more value we can deliver via mobile devices, the better.

5. Retention – This is a reverse model at first, looking at lost customers and opportunities and analyzing what went wrong and how we can fix it. The important question to always keep in mind, ”What can I do for you to make you happier?”

6. Loyalty – This comes from first addressing any retention issues, as noted in #5, and using analysis from the most loyal customers to determine what needs to be consistently provided via communications, content, offers, and customer service.

7. Personalized content – Content is exceedingly important, and the more personalized it is, the better. I remember when PURLS (Personalized URLS) were introduced to direct mail. The result: an instant lift in response by a minimum of a 0.50%.

8. Alerts  – Alerts allow you to inform your customers about improvements in your products and services. It’s important to find the best method to communicate your message, as an alert is helpful in re-targeting consumers who have not yet transacted.

9. Incentives – everyone likes a bonus, look at the cosmetics business for example whether via  web or in-store purchases samples are provided…leads to new purchases and data on what samples are performing better than others to convert into  sales of new products or sale items.    As mention in #1 this will also over time show what incentives/offers  customers like the best and can now be consistent via a Referral Program .

10. Birthday Wishes and Gifts  – A birthday wish is always appreciated especially when it comes from a non-expected source, a birthday gift…well who doesn’t want one of those.

These are the options. Now it’s time to decide what first steps are the best for your business.This process works most smoothly if it involves the right people and the right teams, depending on the size of your organization.

For example let’s say you own two local pizzerias, the counter staff, waiting staff, and delivery staff are the key to the customer experiences and what may be missing from making your customers happier!

Now let’s say you are the CMO of Domino’s with more staff in the three areas…the difference is that you have a lot more staff to communicate and as a result, it would be optimal to set up a simple survey system that lets the employees choose whether or not they want to be anonymous in the system. This garners the most honest data possible.

Domino’s showed the power of analyzing and making data actionable with their revamped online ordering system that allowed customers to watch their orders from start to delivery online.  This is the quintessential system with all the bells and whistles you could think of that creates internal efficiency via transparency.

So to summarize, we hear the following terms quite often these days:

BIG DATA

2nd Party Data

3rd Party Data

However, it all starts with 1st Party Data and the Data in your CRM, your web logs, your POS, etc. but as mentioned above in the two examples of a local pizzeria to a major chain your employees that are the stakeholders in deploying your customer service strategy can provide amazing “hands on” experiential data.

Lazlo took forever to Graduate and win the grand prizes he sought after and hacked  in life!

Lazlo 2

So I sort of made it past high school but have been very lucky to have owned and partnered with smart companies…the technical term for people. Please keep this in mind as some of this post might not even be using the right academic terminology but you will get the gist!

Also keep in mind when you’re an entrepreneur real life support is needed and very hard to find I’ve been blessed with that as well along the way friends and family. We do lose some of them along the way, I have! It can be a painful journey even when you are at the top of your game.

Yes yes we read books, we go to seminars, we google away we can find anything we want to learn about and for those in the data class we use tools and many of them quite expensive to research humanly the analytics with machine…but I hear college is very expensive too!

So I have little experience in the college part but will say that my right friends that are also mentors (my homies and they know who they are) most of them are super educated…together we worked and I studied my ass off enough to be able to keep up with them, learn from them and even hire people like them.

Then it was game on I worked with my professors everyday to win and build business.  These are real friends they never judged my background rather they appreciated and respected my “street experience.”  Thank you all!

I’ve definitely taken quite a few finance classes, thank goodness for QuickBooks (really not to many other options over the years although  there are more start ups coming in this space) but for about 20 years that software has been a part of my finance class. Every time they make a change or enhancement a meeting to research whether or not we should upgrade or use it is needed etc…I’m learning, getting a credit or something like that in finance here.

This happens across so many parts of my life and work as I am sure in yours as well.
So I just switched my Masters to Listening! 40 years old and still in school and now adding a new Masters Degree.

After all these years of slicing and dicing and mixing and matching and breaking and building of data and my career…new data keeps coming and I need to keep learning.

Now we have this amazing social listening ability so I’m adding this Masters to the data analytics Masters program I’ve been in all 20 years of my work life.

Recently I needed to add new and unique services for companies, increase tech and data partners create new collateral, nail some case studies from the sampling and staging and sampling and staging then go to market because I added a Masters that will enhance my products/services.

So at this rate from a work perspective and especially life I feel like I am never going to GRADUATE, this is AWESOME I will get to hang out with new students and teachers of all ages and backgrounds with so many perspectives and plans and passion and we can learn from each other.

So I get to be Lazlo, my favorite character in Real Genius, the only character in the cast with a character name playing the actor himself.

Jon Gries  – Lazlo Hollyfield (as Jonathan Gries) Real Genius Cast IMDB

What I’ve Done Wrong Along the Way…

Posted: September 4, 2015 in Posts

This post is like sharing parts of my daily anxiety but yet as well and more importantly how I omit these errors to get the next one right!

First off just the title of this blog post makes it seem like it’s going to be a long read, but I would need days of your time reading about really stupid, boring irrelevant stuff.
But there are some things I think about that I realize recently are what have made me pretty good at what I do (which at times is even complicated to explain) but if you read anything about me or that I’ve posted or even care to know it’s all around data.

19 years of data, I remember in 1998 telling my business partner they don’t even get what they’re buying and building, companies will be able to to to banks and borrow money on data audiences and segments and the goals they create.

Anyway so what I’ve done wrong…I don’t even know all the things there could have been one day alone I did over 100 things wrong, but there is this handful or maybe 10 handfuls of things that just pop into my mind sometimes.

You know the ones that give you the chill up your spine and what feels like an electrical shock, they make me wish I had that time machine, or the worse the ones that it’s like you’re right there in that moment again embarrassed maybe, foot in your mouth, in fear of the outcome, etc.

Homer

I have made many mistakes that I’m really proud of too, they changed and saved my lives and the lives of the people that have relied on me. I’m combining my business and personal life into this post.

I’m sure today alone I will make some good mistakes and some bad ones and I may not know for an hour to who knows when which are which but they are both going to be made with the intention of learning, becoming, better, stronger, faster and more honest with myself.

Look at history alone and we can find tremendous world changing examples of mistakes being both really bad at times and as well really really good.
Well I’m going to go make some mistakes and I see how I and others benefit from them because that’s the goal.

In time I will elaborate on certain ones that I feel may help others make less of the bad ones and more of the good ones, I’ve been an entrepreneur for 19 years I got a lot of both thank God!

So maybe my next post is “Here is what I did wrong this time…”

Have a great day and go make some mistakes…that way you can accomplish some goals.