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

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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.

Customer Satisfaction Surveys (CSS) have become so prevalent that we sometimes lose sight of their original purpose.

Understand the experience from the customer’s perspective to fine-tune service delivery and service offerings.
Assess the performance of our staff so that we can address service delivery gaps and reward exemplary performance.

We might have started off with the right idea but the CSS programs’ primary focus at some companies has certainly evolved over a period of time, driven by different objectives. Here are some of the results:

Selective Survey

Some companies sample the customer satisfaction level at various service delivery milestones. While the original goal was to address service deficiencies as they occur and recover from potentially damaging service failures, it soon became about eliminating dissatisfied customers from the survey pool as a means of keeping the negative reviews away.

If you have had a faulty car dealership experience recently, you may relate to this. In the beginning, you are assured top service by your salesman and a guarantee that come survey time, you will be perfectly happy to give him a 5 (highest score). You are reminded periodically that your satisfaction is paramount and your comments and remarks, valuable. If things go irreparably wrong, however, the reminders stop and the survey never materializes.

Selectively removing your CSS from the survey pool, removes the possibility of a potentially damaging score. The incentives which can run into millions of dollars with some CSS programs are too attractive to risk losing. Likewise, the very important perception that you consistently do excellent work. Why jeopardize both by sending a disgruntled customer the survey sheet that he doesn’t necessarily have to see?

Is it manipulation if you don’t send a survey to a customer that you know is displeased with your service? How about if you send a survey but just don’t remind them? Is it wrong to send repeated reminders and encouragements to customers that you know have had a favorable experience?

It is not so black-or-white and I don’t believe there is anything wrong with fine-tuning results for the best CSS score possible as long as we do not lose sight of the real program objectives.

Encouraging a Good Score

Certainly, it is not wrong to manage the customer experience toward an end result that achieves customer satisfaction and if receiving a good score motivates that behavior, why not? The customer can win from such an equation where service staff are empowered to “make things right” within certain guidelines and limits while reinforcing the motivation to earn a good score.

Perhaps this is one of the good byproducts of the focus on managing Customer Satisfaction Scores. Is it wrong for some customers to receive a different level of service and attention because a particular incentive hangs in the balance of the score?However, it’s not true as viruses are not the only threats and source of infection in diabetic men is http://www.wouroud.com/order-1826 viagra for sale india a possibility. You will definitely find that we really deal s you could try this out viagra prices with good medicine and superb services. That is the reason, it is necessary to know about the generic viagra pros and cons is required before jumping into buying a product for you. Whose bright idea was it to hamstring the Senate with a rule that a bill cannot be brought to the floor for a order cialis online vote, even though most measures only actually require a majority vote.

In some scenarios, one customer score can literally decide whether the CSS score qualifies the company for huge business incentives. In other scenarios, the company may recognize that no further scores regardless of how good they are will move the needle toward the incentive. Will those customers receive a different level of service and will staff be limited in their empowerment to achieve customer satisfaction when the score “will not count”? The answer is yes, the stakes are so high in some cases and the level of sophistication in managing scores is becoming so precise that such decisions are being made.

Virtuous Circle

Government organizations, corporate accounts, and global relocation companies are aware of these opportunities and potentials for abusing score management. Yet, they do not always move quickly to prevent such abuse. In fact, some organizations actually benefit when CSS scores show positive improvement trends regardless of how this is achieved. Think about it, if your organization manages the mobility program well, then the end result is quality improvement as evidenced in the CSS scores. We all answer to our customers, our managers and our stakeholders.

When organizations choose not to recognize the manipulations, it can make some of us question the value and legitimacy of these programs. Take one aspect in our business which relates to filing rates in a program under separate aliases. In some government and relocation company programs, forwarders are allowed to file under multiple Transportation Service Provider entities. These entities may actually be owned or in some cases managed under common financial and administrative control.

In one such program, the business is dominated by companies that control over 100 Transportation Service Provider entities each. This allows them to file stacked rates which effectively gives them control to manage the pricing offered. In peak periods, they can “blackout” low filed entities due to lack of capacity favoring the higher filed entities. This is not underhanded, it is simply applying the principles of supply and demand. During peak periods, underlying transport and service infrastructure will seek optimum rate levels so the stacking of rates allows this supply/demand equation to work.

It does make you wonder why the organizations sponsoring such programs require this sleight of hand rather than simply recognizing market forces. The capacity of the industry is severely limited in peak periods and rate mechanisms must allow for seasonal adjustments to increase capacity.

What may be a bit more questionable is the strategy of “trashing” and “rebuilding” the scores of these entities. During peak periods, the companies employ a strategy of channeling high volumes of business to an entity that has filed high rates knowing that the score of that entity will be “trashed”. In other words, handling that kind of volume during peak periods will inevitably trash the score ranking of that entity. Some shipments will get trashed and some customers will be disillusioned in the process. These companies know it, they allow for it, they build the cost into the program. In the wings, the company has parked certain entities into a rebuild mode where the scores are closely managed to achieve a high CSS score for the next cycle.

The systems that these companies have developed are sophisticated enough to cycle through the rate filings and scores to achieve some stability and hopefully growth in their businesses. An interesting fallout of this system is that small single entity forwarders are simply not able to compete effectively as they cannot afford to deploy the staffing and systems required. The end result is that the small businesses that the programs were meant to protect in order to promote competition are capitulating and handing over their forwarders to be managed by these larger companies. In a way, these companies are just adapting successfully to the business environment. These companies are aggregating volume, creating efficiencies and as discussed, in some cases, actually improving quality.

As long as we are aware and mindful of the potentials for manipulation, perhaps we can build on this foundation.