Understanding Data Analytics in the Moving Industry

We sometimes refer to our business as being a basic services industry.  Some suggest that the essence or most important element in building a successful service business is just that, service excellence.  Who can argue that the entrepreneurial, owner-operator model can and often does produce some of the best examples of personal service.

When an entrepreneur first opens a simple restaurant, she may be the receptionist, cook, waitress and business administrator.  In those first days of the business, the owner greets every customer personally, attends to them personally and provides a level of personalized service that is hard to match as the enterprise grows larger.  As businesses grown and a structure is implemented to allow it to scale, we lose some of that personal touch.

In this context, a discussion about the benefits of understanding data analytics as a way to better manage our business and growth strategy may not seem intuitive.  Indeed, we should start the discussion by saying that if the first and most important building block of service excellence does not permeate the organization, no amount of analysis will help.  You may be able to drive inquiry and even business to your door through marketing, advertising and analytics but only service excellence will grow goodwill and ultimately your business.

It is a Basic Services Industry

Even though our business may seem simple at its most basic levels, there are also levels of complexity depending on the service lines that we choose to focus on and of course the size, service scope and geographic scale of our business.  As the industry continues to mature, we can find the complexity of multinational, even global companies engaged in moving, relocation, logistics and outsourced mobility services.  The business, however, is still primarily made up of small to medium sized entrepreneurial enterprises (SME) which cooperate closely together.

We will leave the sophisticated analysis to the larger corporations in our business and limit our discussion here to the kind of analytics that might benefit the SMEs.

Data Gathering & Data Integrity

In order to analyze data and turn it into useful business information; you first have to gather the data.  With many SMEs, this can present a challenge.  There is a cost to collecting data and ensuring the integrity of that data.  In some cases, those costs are higher than the potential benefits that the business may derive from analyzing the data.  Each business must weigh that balance carefully and ensure that they focus their limited resources to the aspects of the business that will benefit it the most.

Most companies use automated computer systems to help manage their business.  If these systems are designed and implemented well, then valuable data is a natural byproduct of the company’s operational activities.  If staff members feel that they are inputting data into systems just so management can have access to information, the chances of achieving timely and accurate data input are reduced.  The optimum is to design and implement systems so that you achieve efficiency, accuracy and provide staff easy access to the information they need to succeed in serving the customer.

We will jump into the subject of data analytics with a discussion about the Cost of Customer Acquisition.

Cost of Customer Acquisition

The chart below suggests what the estimated costs of acquiring a customer may be.  Now, you may intuitively know whether the estimated numbers below are in the ball park for your business but how many businesses actually measure and analyze this data?  More importantly, how do we do it and why do we need to?

The sheet below provides one very simple way to calculate CAC.  We add the costs related to Prospective Customer Inquiry (PCI) generation and processing.  These are the total costs we might spend in a year to generate leads (PCI).  In this example, you see the various costs for Advertising, Internet (website), Inside Sales staff and Moving Consultant.  We have purposely kept it very simple.  You can get as detailed as you want in your analysis.

Now we can take that Total Cost of Customer Acquisition and divide it by the different stages of PCI processing.  Initially, we are estimating that we have generated 5000 inquiries for the year for the $105,000 investment.  We can now set up a spreadsheet and divide the CAC by these stages.

So, we see in this example that each inquiry is costing us $21.  Each Opportunity (when we get the chance to quote and provide a proposal) is costing $233.  When we actually book a job, we have secured a customer and we see in this example, the CAC divided by the number of customers is costing us $778 per customer.  The example goes on to estimate the gross margin per customer and we see that after deducting our CAC, we are generating $122 in net margin.  That is not money we can take to the bank, we still have to pay for the rent, utilities, etc., etc.

So, the company in this example for the business line that is being analyzed may not have a sustainable business model.  The CAC is too high.  Or the conversion rates are too low.  Or the margin is too low.  Something needs attention.  I say, for this business line, because business lines like local moving, interstate moving, office moving, fine art moving all have a different CAC.  In some business lines like corporate moving, the CAC may be very high but once the customer is acquired, we can count on repeat business.  So, each type of business will be subject to its own CAC calculation.

Now What?

Data analysis by itself may not help your business but if you use the information as a basis for business decisions, we may achieve great results.  It is important to implement your data gathering and analysis structure so that you can periodically test your assumptions.  This periodic comparison process can point to fine tuning and adjustments that can further amplify good results.

In this example, we have determined that the CAC is too high for the net margin that is produced.  Resist the urge to reach for the obvious reaction which could be to reduce the CAC.  In this example, simply adjusting from the conversion rate from 9% to 11% without any other changes could drastically change this company’s fortunes.

In this fictitious example, it turns out that this may be quite achievable by simply reducing the time taken between inquiry receipt to initial contact from the average of 96 hours to 1 hour.  It turns out that our industry may not be great at responding to customer inquiry on an urgent basis but that will be the subject we will reserve for another discussion.

This all sounds good but how do we get the Data?

Yes, that is a very good question.  In order to perform this simple CAC calculation and implement a solution we would need data on:

• Total inquiries received by the company
• Number of Inquiries that convert to Opportunities
• Number of Opportunities that convert to Customers
• Revenue generated
• Margin generated
• Cost of marketing related to inquiries
• Cost of sales related to inquiries

After therapy you may need other complementary treatments to potentiate the effect of decompression like heat compresses or cold compresses to alleviate pain symptoms (as a result of therapeutic decompression or because of the therapy itself), therapy by ultrasound waves that bounce off the tissue. discount viagra Generally see for more info buy viagra canada pills are quite safe, but overdose of Sildenafil Citrate might lead to heart failure. For putting this in perspective, consumption a single 12oz cup of brewed coffee has about 120mg of caffeine. cialis online appalachianmagazine.com These associate nations cricket should be appreciated by ICC for their performance on World Cup matches. appalachianmagazine.com lowest cost of viagra
Some of you reading this will say that you already have all this available and in place at your company.  In fact, some of you already know your CAC very well and use such analytic tools to adjust your marketing and sales process.  Others may face some challenges relative to structuring a disciplined process to gather good data before you can even get started.

For some, implementation may require a few small adjustments. For others, especially those without an automated operational system; it may represent a major effort.  Each company must decide whether the potential benefits of this kind of analysis warrant implementing a disciplined data gathering structure.

Summary and Conclusions

A major factor in determining whether this kind of analysis is right for your company has to do with the phase of development your company is in.  In an entrepreneurial startup, the owner-operator is so close to every aspect of her business that she knows intuitively by touch and feel what adjustments need to be made.  As the business matures, there may come a time when the structure and processes that have developed over a period of time need to be revisited and fine-tuned.  You will know best when the time is right for your company.

This is just one example of how we can effectively apply data analytics to understand our business better and apply a data driven approach to decision making.  The automated systems that we use to operate our businesses collect a great deal of data as a by product of managing every tasks.  The information is already there but in many companies, it is locked up in the system without easy access.  Understanding what data is available is important but knowing how to turn this into valuable information that can help us to manage our business may be beyond the abilities of many small to medium sized enterprises.

Do you know your:

• Margin by business line?
• Margin by traffic lane?
• Margin by client?
• Margin by Moving Consultant?

Those are just some easy examples.  If you don’t have data analytics to answer these questions, you are either at that entrepreneurial stage of development where you intuitively know what do or you are flying blind and hoping for the best without a clear understanding whether your business is operating at its optimum potential.

One answer is to take the first steps and teach yourself a bit about the subject.  Apply a few steps incrementally and adjust from there.

Another approach is to task someone within the company who has an aptitude to develop such skills or perhaps it may be time to hire someone who has such skills.  Some engage consultants.  The good consultants can lend their experience and knowledge to short cut the process of achieving tangible results.

In future articles, we will delve deeper into this topic of data analytics and explore other aspects of learning to use the information within your business as a basis for better decision making.