The Minimum Price Tag of Traditional Consulting and Data Analytics Services for Small Businesses

This article will be short and sweet. I am going to do baseline estimations of the minimum price tag of data analytics and management consulting comes in from the perspective of a small business owner.

Let’s imagine a small business owner named Natasha. Natasha owns a no-frills, minimalist fashion boutique called Frey.

Frey sells most of its products through its e-commerce website. Her products are exceptional and have been picking up noticeable traction around the U.S. At this stage, she’s looking to grow the business further.

Natasha knows a few things about her customers, but there are many uncertainties.

  • She knows that her customer base is growing, but she’s not quite sure about the main demographic profiles of her customers.
  • She knows her brand is gaining traction outside of her small hometown of Toledo, OH, but she’s not exactly sure where.
  • She also knows that she wants to to launch another ad campaign, but she is unsure what the most efficient way to do this is.

This is where data analytics and management consulting come in.

Currently, there are four main paths that she can take to answer her questions:

  • Traditional consulting (i.e. McKinsey, Deloitte, a local boutique consulting agency)
  • Outsourced analytics (i.e. Data Outsourcing India, Data Entry Outsourced , EDICOM)
  • In-house hiring (i.e. an in-house analyst)
  • Analytics tools (i.e. Qlik, Tableau, Zoho)

These four options all fundamentally solve the same problem for Natasha: they help remove the uncertainty that comes from investing money in an ad campaign.

Let’s start with traditional consulting first.

Pros: It’s the most reliable. Consultants have a lot of experience in different industries and are expected to deliver results.

A big plus is that the project will be a short term engagement so It’s relatively low-commitment compared to hiring a full-time employee.

Cons: Based on interviews and online research, it is possible that Natasha will be charged roughly $50–100/hour at the bare minimum (if she’s lucky).

Let’s assume the project lasts a week, so around 40 hours of work. In total, the project will cost around $2,000–4,000.

Minimum Price Tag

  • $2,000 for one week
  • Time needed to research available consultants.
  • Possible uncertainty of quality when seeking the most efficient option
  • Possible uncertainty of ROI and impact of consultants’ work

Consulting might be the first option many consider but there may be a more cost-effective and sustainable alternative.

Let’s look at outsourced data analytics.

Pros: While price is highly dependent on the scope of a project, a smaller project would roughly be in the thousands of dollars with a lower price floor than traditional consulting. Like with traditional consulting, the project will be short term so it’s relatively low-commitment.

Cons: Outsourced analytics can vary widely in quality so research is important. Also, many outsourced analytics services are typically carried out in places like India or China. In these cases, the language and cultural barrier should not be underestimated. This could potentially introduce friction between Natasha and the data analytics service provider.

Minimum Price Tag

  • ~ >$1000 for one week.
  • Time needed to research appropriate company.
  • Uncertainty in quality if outsourcing to international firms.
  • Uncertainty in ROI and value added to business.

Outsourced analytics might be a huge money-saver, but the cultural differences and distances place many uncertainties on whether the project will succeed.

Instead of looking for pricey external advice, what about an in-house analyst?

Pros: An in-house consultant would always be on call and would be more familiar with your business than an outsider. You only have to provide training about the context of your business and industry once, minimizing the friction of delegating tasks (compared to working with consultants).

Cons: Searching for a good analyst is very time-intensive. Hiring one is a big commitment. Also, Natasha isn’t sure that keeping full-time analyst around would be worth it during the off season. With a minimum salary of roughly $60,000, an in-house analytics specialist would be as much of a luxury as hiring a personal chef.

Minimum Price Tag

  • $60,000 for one year (avg. ~$1150 a week) plus benefits and taxes
  • The search process
  • High commitment
  • A possibly under-utilized employee during the off-season.

Hiring an in-house analyst is the most reliable option, but it is also the most expensive one.

An option that would be a smaller commitment is to empower her current marketer, Sam, with a data tool. Sam is not a data analyst, but he has experience writing ad copy and designing marketing collateral.

Pros: She can do analysis in-house with Sam, who is already familiar with Frey.

Cons: Let’s slow down for a minute…..

This is more complicated than it sounds. While it may seem like a data tool is the best solution, coming to that conclusion is fairly complex due to how data tools are sold and deployed.

In addition to the huge variance in price tag and functionality of data tools, they also have a lot of hidden costs, which causes several main issues:

  1. Buying a data tool locks you in. Data tool companies today typically function with a subscription or SAAS (software as a service) model.
  2. There’s a complex installation and implementation process which may require IT consulting for the change management.
  3. Due to high friction around moving data from one data system to another, switching tools is extremely costly in terms of time and money.

If Natasha goes through all of this effort to get a tool and, after three months, realizes that it was the wrong tool for her business needs, the high switching cost of finding a new tool can be paralyzing.

Furthermore, most data tools require a degree of expertise to deploy and use.

Many tools can create sleek dashboards and charts, but the biggest problem that Sam the marketer would have is how to turn his dashboard metrics into business insights and an actionable marketing strategy.

Even if he sees a clear trend, it’s uncertain whether the tool would give him the confidence needed to make sound conclusions.

Because data is inherently messy, looking at graphs does not equal gaining actionable insights.

Data tools require some level of technical skill in order to properly interpret every graph, p-value, and z-score. Thus, the value of the tool for empowering Sam to perform in-house data analytics is entirely dependent on Sam’s data expertise.

For now, let’s assume Natasha doesn’t realize this second issue because it’s a hidden cost and buys an appropriate tool like Tableau Professional that currently costs $840 a year.

Minimum Price Tag

  • ~$849 a year per user ($70 a month)
  • Time to research an appropriate tool.
  • Time spent asking for quotes and comparing prices
  • Time and/or money (if hiring technology consultants) to deploy and integrate the tool with the business and existing data infrastructure.
  • A high level of uncertainty of ROI due to the fact that Sam likely lacks the expertise to correctly interpret the data.
  • Inflexibility since many tools lock users in for at least a year.
  • Time (2–3 weeks) if she wants to switch tools and deploy the new system

Training in-house talents with an out-of-the-box tool seems to be the easier option on the surface, but hidden cost and hassle can jeopardize your data project.

Conclusion

While the data tool solution is best for Natasha’s price range, this is also the option with the greatest amount of uncertainty due to the high commitment and the expertise requirement.

That’s the heart of what we at Humanlytics are aiming to do: building a low-commitment and cost-effective platform that assumes the user has no prior training or knowledge in data analysis.

As a human-centered data software company, we’re not looking to make another data visualization tool for the sake of generating pretty pictures.

We want to be educational and empowering for untrained users while also having high impact in growing a business using data; we want to start by guiding users like Natasha and Sam through the analysis of Google Analytics data step-by-step, delivering powerful data-driven insights that only a computer can give, and providing a rationale for each insight.

Imagine having an app, with thousands of similar analyses and business cases under its belt, telling Natasha and Sam who and where her best users are, and how the algorithm reached that conclusion.

Compared to these other choices, this would be clearly be her best option, giving her actionable business insights with low risk, high certainty of impact, and at a price point that’s affordable to small businesses.

This is our vision to make data analytics accessible to every person and every organization. Follow us on Medium if you share our vision for a data-driven future for all the small-time entrepreneurs and budding business owners out there.


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