Category: analytics

Are You Ready for AI?

Are you ready for AI?Artificial Intelligence (AI) is coming.  It is not a question of if, but when.  Are you ready?

While consumer AI is already in use by companies such as Netflix for movie/episode suggestions, very few companies have implemented AI.  A lot of companies claim to have AI and some offer AI solutions, be careful with what AI really means.

Artificial Intelligence refers to process automation, letting computer programs and hardware take over repetitive tasks.  Machine Learning is add-on to AI in respects of letting the machine learn from new data automatically.  AI can be enhanced through further human based programing.

AI Requirements

What does your company need for Artificial Intelligence?

To start, you need to identify one or more business processes which would add value to automate.  Start with the simplest and most repetitive tasks for your account managers, sales reps, and other operational employees.  Start with a task as simple as retrieving information needed to make a decision.  Many of our clients have discussed the concept of the virtual analyst, a robot like tool which dispenses insights instead of logging on to Tableau to retrieve.

Next, you need good data.  As the saying goes, data out is only as good as data in.  While data is never perfect, even in the most highly controlled environments, there needs to be a fair amount of stability.  While most AI can learn to deal with bad data, minimizing errors is key.

You will also need a good data science team.  You will need team players who can code using a variety of tools, such as Python and R.  You will also need resources familiar with the cloud as AWS offers many advantages, particularly for big data projects.  Even with a giant budget capable of purchasing off the shelf tools like IBM Watson and DataRobot, your data science team will bring the knowledge and experience needed to make these tools even better.

AI Suggestions

When getting started with AI, we have some suggestions.  First, start small.  It is far better to under-promise and over-deliver.  Start with a well focused use case which offers data stability and drives value to the organization. Something like the virtual analyst is a popular place to start.

Second, be flexible with your roadmap.  Count on delays, mistakes, and learnings throughout your implementation process.

Third, find a passionate, progressive executive to be your champion.  Having executive sponsorship not only helps to secure funding, but helps give the project visibility and defense at the highest level possible.  Having an executive on board helps tie the project to company wide goals.

Finally, be patient.  AI doesn’t happen overnight.  In fact, many of the most hi-tech and innovative companies with huge budgets are struggling with AI.  AI is possible, but there isn’t a well refined recipe available, yet.  Keep your eye on the prize and keep learning.

The first one to master true AI will have a huge advantage as the world of technology and analytics progresses.   It is a pretty sweet prize.


Let’s Discuss Data Rights

Data Rights and youData rights is becoming a topic we can’t avoid discussing any longer. With the rise of AI and algorithms, the data companies collect from us is now starting to be used, if I dare use the word, against us. We are in a digital economy which needs boundaries.

We need to start talking about data rights.

First, let’s understand what data companies collect. The last few EULA’s I read mentioned collecting data, but didn’t spell out exactly what data that was.

Second, we need to understand how they use this data. Are they just storing it? Doubt it. Are they using it to help target products to us? Are they using it to push us away from news? Inquiring minds want to know!

Third, who uses this data. It is one thing for an internal analyst to use an anonymized version to understand buying and usage patterns. However, it is something completely different for a company to sell/give the data to a 3rd party who then uses it in a variety of ways. Example: Alteryx buys a data dump of the Equifax data to resell to marketers. This is fine, as long as they are transparent.

Fourth, lastly, we need an opt out method. Just like unsolicited magazines and emails, consumers must have a choice. A choice beyond cancelling their account.

Let’s start the conversation and make it into a movement. I welcome your comments. Please use hashtag #datarights

Thank you!

Why We LOVE Alteryx

Alteryx Data BlendingAlteryx is a data blending tool.  Much like your Black & Decker blender makes short work of blending your favorite fruits and vegetables into your morning protein shake, Alteryx makes short work of disparate data sets. In fact, Alteryx loves the most horrible data you can throw into it!

There are a few more reasons why we love Alteryx:

  • Automatic Documentation – each tool module is easily readable and the step by step process breaks complex data transformation down to small bits.  If you are anal about documentation, you can also use a Comment box.
  • Colors – Using Comment boxes and Tool Containers, you can color code sections of your workflow to discern different data flows.
  • Data Everywhere – at the click of a button, you can pull in your nastiest of data and tame it.  Then, you can push it out to as many output as you want.  What the heck, in addition to Tableau, let’s put the data result out in Excel on three different network drives, push it to PowerBI and why not just drop it in a shared Google Doc.
  • Fast, Pretty Data – Alteryx can take horrible data and transform it into a thing of beauty in a matter of hours or days, not weeks or months.  If you are tired of waiting for your Business Intelligence team “to get around to it”, get Alteryx and sidestep them.
  • Analyst Style, Preserved – There are a number of different ways to do the same thing.  If you need to summarize your data, join it, pivot it, and do a few jumping jacks, the combination of tools allows for multiple solutions to the same problem.  Very much like Analysts love their VLookup or their Index/Match (both do the same thing), Alteryx lets the Analyst be who they are… and that translates to productive creativity.
  • Server Means Collaboration – Analysts across the company can save their workflows to a centralized enterprise server and access each other’s work.  They can even execute workflows.
  • Alteryx is just a great company. – I worked with Alteryx and am a former employee, so I might be a bit biased.  But, Alteryx is a great company.  The “Alteryx for Good” campaign gives back to the community.  They also make Alteryx free to qualifying non-profits.  Awesomeness!

If you haven’t explored Alteryx yet, do!  Don’t let the possibilities overwhelm you.  Take the time to research it and test it.   We guarantee you, you WILL love Alteryx as much as we do.

The Power of Sales Analytics

sales analytics with AlteryxThe Power of Sales Analytics is something every serious sales professional must understand. Sales Analytics is the lifeblood of every sales team, big or small.  The data-driven marketplace means teams must leverage their own data, 3rd party data, and do the digital due diligence to succeed.

The Power of Sales Analytics

There many ways which analytics drives success on the sales team:

  • Team Performance
    • well timed performance reports and scorecards across the sales hierarchy is a must
  • Productivity Insights
    • find out how many prospect touches it take on average to develop an opportunity
  • Account and Prospect Insights
    • who are you selling to, what solutions do they need, and where can you reach them
  • Correlation
    • find out the messaging and market relationships which drive your prospects
  • Marketing ROI and Messaging
    • what role does marketing play in leads, opportunities and revenue generation
  • Compensation Design
    • are you reps fairly compensated for the behavior which drives revenue
  • Territory Configuration
    • have you optimized your territories and set appropriate quota

Enabling Sales Analytics

Even before you can take advantage of the above, there are a few steps which the team must beholden before a true analytics program can succeed. These include:

  • Best practice data capture with your CRM (, Miscrosoft CRM, vTiger)
  • Understanding your sales journey and converting into stages and sales milestones
  • Establish a data-driven culture – reps, managers, and senior leadership must trust the data
  • Use the data to define compensation plans and territory configurations
  • Establish a regular pattern of data cleansing – your reports are only as good as the data you put in

With these concepts in mind, you can leverage the power of sales analytics.  We capture data everywhere. Not leveraging the data is a big mistake which is easy to make.  Whether you bring a team on-board internally, or outsource to a consulting like Spiral Analytics, there is no excuse for missing the power of sales analytics.

Getting Started with Pipeline Management

pipeline management the easy wayHitting your quota number at the end of the quarter is all about how you manage your pipeline.  This post is to give you an understanding of how KPIs play into pipeline management.

Know Thy Territory

First, understand your territory and the quality of your accounts.  Start by putting your accounts into three categories: A, B and C.  A’s are prime accounts, whereas C accounts are not likely to buy/engage.  Account scoring is a topic we will cover in another post. You also might be interested in our “Customer Scoring” course once it is released.

Analytics of Pipeline Management

Beyond knowing your quota number and who is in your territory, there are a few key performance indicators (KPIs) you need to track to help make more sense of your quota number and your pipeline.  These include individual sales rep metrics include:

  • Average Deal Size
  • Average Sales Cycle (SQL to Close – depends on playbook)
  • Lead Conversion Rate
  • Average Close Rate

Using these four KPIs and doing your own calculations, you can break down your quota number into something that makes a lot more sense. “Do the math” as we say and your quota number is a template for opportunities where you plug in your accounts.  The math tells you how many deals you need.

As a sales analyst, it was interesting to see which reps looked stressed when they saw their quota for the first time versus the reps who were calm, cool and collected.

We are not going to walk you through the actual math in this post (I know, sorry, but this post is teaser), but we will give you this quick tip:

  • Saalun Quick Tip: About half of your quota number should already be nearing close in your pipeline as you start the quarter.

Early Preview

Knowing the who’s who of your territory and doing a little math behind your number makes pipeline management a breeze.  Of course you have to build relationships and sell, but knowing the math is a huge step ahead.

As we get started, we encourage everyone to signup for our early preview mailing list.  Saalun will be offering more info as our courses are ready to launch and discounts on memberships. Join here.