Shane Jamison

High Performance Sales Coach | #1 Sales Coach | Helping Sellers Blow Past Quota (Click Link Below to Book 30 min With Me)

Give me a motivated seller and I’ll shape a true sales pro. 

Over a 20-year career, as an AE, sales leader, and advisor, I’ve learned that sustainable success comes from refining raw skill into deliberate craft. 

My coaching is equal parts skill-building and mindset calibration. 

I zero in on how you think, prep, listen, and respond, then polish each facet until prospect interactions feel like second nature. 

Clients credit me with turning reactive sellers into composed deal strategists who know their value and project it on every call. 

That’s the craft I bring and coach every day.

Executive Member

Data Analytics 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐭 | Business Growth 𝐂𝐨𝐚𝐜𝐡 | AI Transformation 𝐀𝐝𝐯𝐢𝐬𝐨𝐫 | Fractional CDAO | Economist & Statistician | Mentor, Educator, & Author |

Every founder or C-suite executive knows this:
- Business dataflows and analyses exist.
- Dashboards and decision pointers abound.
And yet, there is hesitation with decision-making.

Most business problems can be solved:
- with the 𝗿𝗶𝗴𝗵𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 of the 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 
- using the 𝗿𝗶𝗴𝗵𝘁 𝗺𝗼𝗱𝗲𝗹 and the 𝗿𝗶𝗴𝗵𝘁 𝘁𝗼𝗼𝗹𝘀
And often by working with 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗲𝗿𝘀𝗼𝗻.

When descriptive reporting is insufficient and decisions need to hold up under scrutiny, 
𝗜 𝗽𝗿𝗼𝘃𝗶𝗱𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲.

𝗧𝗵𝗿𝗲𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝘀𝘄𝗲𝗿𝗲𝗱 with statistical rigor can help:
1. What actually matters right now
2. What will likely happen next if nothing changes
3. Where effort should be applied to change the outcomes

My work sits at the 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗷𝘂𝗱𝗴𝗲𝗺𝗲𝗻𝘁. 

My role as a 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 typically includes:
  • Identifying which questions are decision-critical and which data is noise
  • Building predictive, regressive, and optimization models that reflect real constraints
  • Validating forecasts so risk is understood, not hidden
  • Designing dashboards and lightweight web apps that exist to support action, not exploration

My method is simple:
* Collect and organize relevant data
* Analyze it for patterns and root causes
* Construct models based on those patterns
* Convert results from models to action items

I used my own method to create the 𝗣𝗼𝘀𝘁 𝗶𝘁, 𝗦𝗮𝘃𝗲 𝗶𝘁 app for LinkedIn content strategy.
- Posts are treated as experiments. 
- Performance is measured. 
- Patterns are analyzed. 
- Decisions follow.

I am also the designer and COO of 𝗥𝘆𝘇𝗮 𝗖𝗼𝗻𝘁𝗲𝗻𝘁, which applies this analytical approach to content systems for executives who want consistency, growth, and repeatable outcomes. 

At Ryza Content, content is treated as an applied analytical system through disciplined creation, execution with reporting, and team training.

If you are making revenue, growth, or investment decisions with analysis you do not fully trust, that is the moment I am most useful.

I do not work with teams looking for generic dashboards, surface-level analytics, or motivational content strategy.

If you want clarity before committing more spend, headcount, or time, book a short call:
https://calendly.com/k-lehenbauer/15min

Dr. Kruti Lehenbauer

Do you believe that "Honesty Is the Best Policy?"

Most brands say “we’re honest.” Few wire honesty into how they manage bad bets, surface misalignment, and rewrite the story when reality moves. Where is your system still protecting an old narrative?
Here are the key takeaways from the #PostitStatistics #newsletter Edition 79. Writing them here since nobody seems to actually read the newsletters. 🤣 

1. Treat your company like a research program: protect the core, experiment ruthlessly around it.
2. Shorten the lag between “we noticed a problem” and “leaders discuss it” to cut hidden costs.
3. Pay attention to the speaker who brings the uncomfortable slide, not just the person who launches something new.

Hope you're having a wonderful Sunday!
- Dr. Kruti Lehenbauer

P.S.: Hey Timothy, Ryza Content Creator helped me get this image out of my rambling!! LOVE using it!!

I build Data-based Apps, Solutions, and Systems for SMBs | Data Scientist & Economist | AI Startup Advisor

View newsletter: Post It Statistics by Dr.K

How many times have we heard these terms in various forms when reading about companies. "We are an honest company." or "We do everything honestly." or

Open article: Honesty Is Not a Policy, But A Principle! by Dr. Kruti Lehenbauer • 4 min read

Honesty Is Not a Policy, But A Principle!

You wanted a sandwich, but all you got was a salad!
(Based on the AI sandwich analogy by Rosanne Werner)

Most of us blame AI tools when its output is sloppy.  
The real issue lies in the bread (or the lack thereof).

The AI Sandwich is simple:  
--> Humans frame.  
--> AI tools fill.  
--> Humans finish.

However, without the right bread, the filling is just a salad!

Actionable Insights:

1) Bottom Bread: Upgrade the quality of choices.
• What exactly are you creating with AI? Why?  
• Be explicit about what outcome you really want.  
• Define what *must not* happen, not just the outcomes.  
Result: AI stops guessing and starts operating inside your strategy.

2) Mid-layers: AI is a pattern engine, not a real expert
• Use AI tools to explore, not to decide.  
• Ask it to draft outputs with strict guard-rails.  
• You choose which pattern matches the context.  
Trade-off: Choices take time, but that is where the edge lives.

3) Top Bread: Decision-making cannot be delegated to tools.  
• Human refinement is not “proofreading”; it is meaning making.  
• Sanitize for bias, cliches, and misaligned objectives.  
• Inject lived experience and examples in your decisions.  
Advantage: This one seals the deal on a great outcome.

AI can generate options.  
Only you can decide what serves your appetite: 
A tasty sandwich or a sloppy salad!!

🔔 Dr. Kruti Lehenbauer & Analytics TX, LLC
#PostitStatistics #AI #DataScience #Economics

P.S.: The original AI Sandwich post by Rosanne: https://lnkd.in/gsMsr97p

P.P.S: What is your favorite type of sandwich??

This one is almost embarrassing to write about...

Edition 78 of my #PostitStatistics #Newsletter comes straight from a real experience that caught me off‑guard for a few minutes longer than I’d like to admit.

A few days ago, I had a close call with a very convincing “literary agent” outreach. It was polished, friendly, and almost believable... until it wasn’t. 

That moment sent me down a rabbit hole of how scams have changed and why even the most alert among us can get momentarily pulled in.

This newsletter is part story, part system, and part reminder that our expertise has value… which means we have to protect it.

Hope reading it proves to be useful for you in some way, shape, or form!

Have a beautiful Sunday, y'all!
🔔 Dr. Kruti Lehenbauer

P.S.: If you haven't registered for the free workshop on "How to Create Social Media Content 10X Faster?", check out the link in the Newsletter for more details! 

P.P.S.: Have you ever "almost" fallen for a scam? Or am I alone in getting carried away for a bit?

Data Scientist & Economist | Building Data-Led Apps and Systems for Founders & SMB Leaders | AI Startup Advisor

I think of myself as a pretty tech-savvy and alert individual, who is usually aware of scams and scammers. However, a recent experience threw me off-g

Open article: A Scam That Almost Worked... by Dr. Kruti Lehenbauer • 5 min read

Are you NICE or are you KIND?

Yesterday, I was talking to my BFF and her words hit home:
"I am done being nice to people. I am and will remain kind. 
But niceness is a whole another thing."

And I realized she was right. 
Being nice and being kind are not the same thing. 

The former hinges on being liked by others while
The latter hinges on truly liking others!

In advisory work or consulting, this becomes a quiet risk: 
Do I want to be liked, or do I need to be useful?

In data, strategy, and systems, this risk can be egregious.
Speaking up might not be popular, but it is often necessary!

Niceness is present-focused:  
- It calms the room.  
- It protects the relationship.  
- It keeps the contracts safe.

Kindness is future-focused.  
- It holds the line on reality.  
- It accepts discomfort as the price of truth.  
- It protects the trajectory, not the mood.

Here is the trade-off that many miss:
* Every time you avoid a hard conversation, you compound hidden risk.  
* Every time you soften a strategic flaw, you extend the life of a bad model.  
* Every time you dodge operational rigor, you increase the cost of future change.

Kindness sounds different in practice:
1) You challenge data comfort.
  You do not accept “good enough” numbers.  
  You trace anomalies, question sources, and force clarity on what is real.

2) You confront strategic inconsistency.
  When story and data diverge, you side with the data.  
  You name the gap, and you sit in the tension with the client.

3) You design for the future, not the meeting.
  You recommend changes that might make you unpopular today  
  but make their business more resilient tomorrow.

Kindness is not cruelty.  
It is disciplined care, aimed at outcomes instead of approval.

🔔 Dr. Kruti Lehenbauer of Analytics TX, LLC
#PostItStatistics #DataScience, #AI #Leadership

P.S.: Are you choosing short‑term harmony over long‑term honesty in your own work?

Still writing every post from scratch?

If content takes hours, your system is broken.
Learn how to turn one idea into posts, visuals, and videos in less time.

1️⃣ Turn your existing expertise into reusable content themes so you never start from a blank screen again.  
2️⃣ Design one core structure that adapts across platforms instead of creating separate posts for each channel.  
3️⃣ Build a light review loop that shows what works so you can double down instead of guessing every week.  

Join our LinkedIn Live workshop on Jan 15th at 3 PM CST and start creating your social media content 10X faster.

🔔 Dr. Kruti Lehenbauer 
#ContentStrategy #AI #DataScience #RefreshWithRyza

What is your AI dashboard not telling you?

We have more AI dashboards than real decisions.  
Pretty charts. Fast outputs. Shaky understanding.

AI tools make analytics look easy.  
So, leaders assume the thinking is handled.  
Newsflash: it is not.

AI is brilliant at pattern speed. It is terrible at context, risk and price of being wrong.

Actionable Insights to make your AI analytics meaningful:

1) Treat assumptions with caution 
-Most AI tools quietly freeze yesterday’s world into today’s logic.  
-Markets change, behavior changes, input data drifts.  
-Schedule assumption reviews like you schedule audits.  
-If the world has changed, the model’s “intelligence” is now risk.

2) Separate signal from convenience  
-Dashboards optimize for what is easy to visualize.  
-Businesses need what is hard to question.  
-Ask your data expert: “What are we not measuring that could change this decision?”  
-If that answer feels uncomfortable, you are getting closer to truth.

3) Stress test against edge cases, not averages  
-AI tends to be trained on the middle.  
-Real losses live at the edges.  
-Walk your data expert through worst case stories: “Show me what the model does in a sudden demand spike, a price crash, or a region blackout.”  
-You are not predicting the future. You are mapping failure modes.

4) Pair domain experts with data experts  
-A model may see a correlation.  
-A domain expert knows whether it makes sense.  
-Put product, finance and operations in the same room with your data lead.  
-Review one critical model together and ask: “What decision would this push us into that feels wrong in real life?”

The core idea:  
AI can scale analysis.  
Only humans can own judgment.

🔔 Dr. Kruti Lehenbauer of Analytics TX, LLC
#PostitStatistics #RefreshwithRyza #AI #DataScience

P.S.: When your AI output conflicts with your gut, who wins, and why?

Struggling to create content for social media?
You are not alone in feeling like this. 

Most creators and teams do not struggle with expertise. 
They struggle with turning that expertise into clear posts 
Without losing hours to writing, editing, and reformatting.

Our pursuit of perfection often makes content creation harder than it needs to be.

On January 15 at 3:00 PM CST, Timothy Goebel and I are 
Hosting a LinkedIn Live workshop on:
"How to Create Social Media Posts 10X Faster?"

We will walk through a practical system for:
1️⃣ Turning your themes, research, and experience into post-ready content
2️⃣ Maintaining a consistent voice across text, visuals, and short videos
3️⃣ Reducing time spent staring at a blank screen
4️⃣ Reviewing what works and improving future content intentionally

This is a process-focused session designed for creators, SMBs, and teams who want consistency without sacrificing quality or credibility.

We will be showing the principles upon which Ryza Content Creator was built, but you will be able to implement the methods using your own tools.

- Dr. Kruti Lehenbauer
#PostitStatistics #RefreshWithRyza #ContentStrategy #AI #Data

P.S.: What is the ONE barrier to creating posts that you'd like for us to cover in this event?

Ranbir Bhutani, Susanne Mueller, MA, PCC (she/her) and 1 other connection attending

How to create Social Media Posts 10X Faster?

Are you stuck in a Build vs. Buy quandary?

One of my clients was and here's what happened:

Most teams treat deadlines like a moral obligation.  
Ship the feature. Hit the date. Absorb the cost in silence.

Tech Solutions (pseudo-name used) did something different.  
They priced the cost of speed instead of guessing.

Their tension was simple to describe, brutal to navigate:  
Wait, and watch revenue slip.  
Rush, and drown in overtime and license fees.

So, they stopped arguing in meetings and built a data model.  
Hours, overtime, and one‑time licenses became constraints.  
Not vibes. Not opinions. Actual limits. 

The insight was uncomfortable:  
You can “afford” to be late emotionally, long before you can afford it financially.

Linear programming turned that into structure:
1) Treat overtime as a lever, not a failure  
When overtime has a transparent price, leaders can use it deliberately to protect revenue, not reactively to rescue poor planning.

2) Anchor decisions to portfolio impact  
Instead of asking, “Can we build this feature?” they asked, “Which mix of build/buy keeps our products viable?”

3) Use models to expose non-obvious buys  
Buying one feature looked expensive in isolation, but cheap when it unblocked other products and conserved internal talent for differentiation.

4) Make “what-if” a standard practice  
When assumptions change, you don’t restart the debate; you re-visit the model. The conversation shifts from ego to explicit trade-offs.

In dynamic environments, the real advantage is not the perfect forecast.  
It is a reusable decision system that survives changing conditions.

🔔 Dr. Kruti Lehenbauer of Analytics TX, LLC
-----
REFERENCE: Tech solutions case study by Dr.K.
Output generated in: Ryza Content Creator 
#RefreshWithRyza, #DataScience, #PostitStatistics, #AI

P.S.: Where in your roadmap are you still paying for speed with gut feel instead of a clear, modeled trade-off?

Want to boost your content creation by 10X?

Highly recommend checking out Ryza Content. 
I'll bet that between Timothy and I, 
We have over 2,000 hours invested into this application!
It is a true labor of love with 4 AM text messages and
Fixing breaks & errors even on Christmas morning!

But that is what you get when you refuse to engage in vibe coding
And instead create something with a unified vision and goal. 

What we have and have embedded into Ryza: 
- A dogged determination to use AI responsibly
- An unyielding adherence to our principles
- A clear vision of what creators need
- A thrill of building something new

A content creation system that allows everyone to grow.
Whether they're creating for LinkedIn or Facebook or Instagram,
Or just wanting to establish a social media presence for business! 

Check it out and let me know if questions!
-Dr. Kruti Lehenbauer

Why do you have content creation limits on Ryza?

A potential client asked us this question recently.
Both of us almost simultaneously answered 
"To help you meet your goals, correctly."

Back when we first started building this app, 
We had detailed discussions on what are the right limits
To impose on content creation for each type of user. 
In fact, our entire tier structure is based on the goals we have seen.

Independently, we both started as individual creators on LinkedIn.
Over time, we started posting on multiple platforms to establish brand.
With the launch of Ryza itself, we slipped into business mode.

And we've seen firsthand what happens when you overproduce
Content or posts, by setting aside X hours one day a month.

1️⃣ You forget what you're posting about today.
2️⃣ Your scheduling replaces chance of spontaneity.
3️⃣ Your voice and content become repetitive and bland.

Here are our tier-specific details:
• Individual Creator ($50/mth): This entry-level tier is primarily focused on building a social presence on LinkedIn. It allows for the creation of one post and image per day, totaling a maximum of 30 per month, alongside four short videos monthly.

• Influencer Creator ($200/mth): Aimed at building a multi-platform brand presence, this tier significantly increases the daily volume to seven posts and images. However, the monthly maximum is capped at 40 posts to maintain quality, and video production is increased to 10 per month.

• Solopreneur Creator ($500/mth): While the post and image limits remain the same as the Influencer tier (7 per day, 40 per month), this tier is geared toward business growth. It offers a higher video limit of 15 per month and introduces a strategic limit of 10 Ideal Customer Profile (ICP) lists per week to aid in lead generation.

• Business Creator (Custom): For organizations and teams focused on brand growth, all output limits (including posts, videos, and ICP profiles) are customized based on the organization's specific needs.

Strategic Rationale for Limits:

Unlike many AI tools that prioritize volume, Ryza Content enforces these daily limits to ensure quality over volume. 

These "operational boundaries" are intended to protect the user's brand integrity by preventing the AI from deviating from the creator's established tone, a phenomenon the sources refer to as "voice drift".

To visualize this, the subscription tiers are like different sized fuel tanks for a vehicle: each is sized to ensure you have exactly enough to reach your specific destination (social presence vs. business growth) without overextending and risking the quality of the journey.

- Timothy Goebel & Dr. Kruti Lehenbauer
#ContentStrategy #BrandPower #QualityMatters

P.S.: Questions or concerns? We are just a DM away!

How much can you trust your AI Dashboard?

I hope you all enjoy watching this short video and reading the note on why the HUMAN-ON-THE-LOOP is soooo critical in the world of #AI and #DataScience.

- Dr. Kruti Lehenbauer

#RefreshWithRyza

Is your AI dashboard quietly risking real money?

AI tools make reports look sharp, fast and smart. 
But pretty charts can hide bad assumptions. 
Models age as markets change. 

Even huge players like Zillow paid the price when their valuation model drifted from reality. 

Use AI to crunch, humans to question. 
Schedule model reviews, challenge the logic, stress test against real conditions. 

When did you last ask, “Does this still make sense?”
- Dr. Kruti Lehenbauer

#AI, #DataAnalytics, #RefreshWithRyza
P.S.: This post was created using Ryza Content and Dr. K's post from earlier this week.

Data Leader: "No, we can't do this with what we have."
Non-data Leader: "I just asked GPT, and it says we can. Find a way."

This is not a joke... it is becoming a reality across organizations.
So let me say it bluntly:

False data science is WORSE than no data science.

GenAI tools don't seem to get it and throw data terms around casually.
And because it sounds "data-related" and "data-driven", many just follow it.

Let's consider the example of Normalization of data. 
Everyone uses it, few respect it, and even fewer really understand it!

The pattern I see in businesses and startups:
--> Leaders ask GenAI to compare trends 
(Because asking their data teams would be difficult, I guess)

--> GenAI cheerfully replies: “Just normalize with min‑max.”
(Because the relative spread is all that matters, isn't it?)

--> Leaders get formulas from GenAI and insist on using them
(Because if GenAI gave this answer, it must be 100% reliable)

No questions about populations or sample sizes. 
No investigation of data distributions. 
No real business context utilized.

And when, the results don't match up to expectations, 
It is easy to point fingers and say, "probably the data team's fault."

Min‑max can work when you have tiny datasets or are prototyping an app.  
It establishes a very low threshold that allows basic testing in development.
However, most businesses are sitting on years of operational data.  
Apply naive min‑max there and you flatten signal, distort outliers, and confuse your own dashboards.

Z‑score normalization is often suggested, but even that gets mangled:
Mixing median with standard deviation.  
Ignoring sample size in the formula.  
Skipping the Empirical Rule, which is the whole point for interpretation.

Actionable Insights based on Z-scores:

1) Treat data science as a policy, not a trick  
Agree as an organization: when do we use Z‑scores or find alternatives. 
Consistency beats cleverness.

2) Anchor decisions to the Empirical Rule  
Use Z‑scores to define “normal”, “watch”, and “intervene” bands. 
That keeps performance conversations fair across teams, products, or time.

3) Design dashboards around distributions, not single values  
Show how many observations sit within 1, 2, 3 standard deviations. 
This helps leaders see whether they are managing a stable system or chasing noise.

4) Make “which normalization did we use?” a standard meeting question  
You would never accept “a model” without asking for the method. 
Apply the same discipline to scaling. 
It changes the conclusions you draw.

Data Science is less about math and more about governance.  
The math is easy to solve; the discipline is not.

-Dr. Kruti Lehenbauer & Analytics TX, LLC
#DataScience, #PostitStatistics, #AI, #RefreshWithRyza
P.S.: Next time you see a “normalized” chart in a meeting, will you know which method created it?

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The Battle

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