For Transportation, Data-Driven Marketing Isn’t an Option, It’s a Requirement

There is a clear marketing advantage derived from improved efforts related to data and analytics in transportation. On-time shipments and quick, effective resolutions to issues like breakdowns and delays have a major impact on how your customers view your business and serve as a differentiator when they compare you to your competitors.

It’s now a given that companies that want to maintain and grow their position within their industry need to understand, apply and develop a deep understanding of data-driven marketing. This is especially true in the transportation industry — our focus in this article — where a variety of factors have delayed the implementation of data analysis and its application to marketing.

Currently the transportation industry is in its most favorable position ever, and is benefiting many businesses within or associated with it. Robust economic conditions, coupled with a massive driver shortage, have led to a severe capacity crunch that has enabled price increases, more selectivity with loads and destinations, and a significant amount of control in the hands of providers — a far cry from the state of the industry 18 months ago.

However, while it’s important to reap the benefits of the current state of the market, change is inevitable. Businesses face a critical need to adjust to the realities that will, sooner rather than later, transform the transportation industry — and addressing a backward state in data acquisition and analysis should be at the top of the agenda.

Addressing the Industry-Specific Barriers That Hinder Data-Driven Marketing

In the transportation world, the largest industry leaders haven’t made data analytics a priority, whether in marketing or many other areas of operation. When compared to other industries, transportation is in fact considered somewhat of a laggard. How can the industry address this?

In our experience working with transportation companies, it’s common to see access to data restricted to information technology staff and other levels of senior management, such as safety and compliance. This means no one else in the organization can utilize the gathered information and apply it to routine but vital marketing tasks such as recruitment and retention of drivers or improving route planning to increase efficiency and customer satisfaction. The legacy, siloed platforms most companies have in place for collecting and analyzing data fail to make it available for strategic decision making. Indeed, very few organizations are modernizing their systems sufficiently to maximize the value of data. Broader and more transparent access to data across the entire organization would lead to better, more measurable outcomes.

This lack of transparency, as well as companies’ outdated systems, make it harder for the industry as a whole to recruit top talent — especially for cutting edge tech positions such as blockchain specialists, data scientists, analysts, etc. A culture change that emphasizes, organization-wide, the value of data and its impact on marketing efforts and many other operational considerations is vital for future improvements, from implementing more effective solutions to hiring top-level talent.

Organization leaders will have to communicate this shift in attitude by explaining the operational improvements to be gained by changing systems that have long functioned adequately.

The end results will be worth the pain of change: improved marketing through more thorough access to data, more actionable insight into operations and an increased ability to recruit talent with the skills needed to help companies reach these objectives. Effective analysis reduces cost and complexity of a variety of core business functions by finding more efficient workflows and identifying opportunities and risk that may have gone unnoticed in the past.

How Data and Marketing Come Together in the Transportation Industry

Amazon’s entry into the transportation world is one of the clearest industry-specific examples of how an organization committed to using data and analytics can quickly disrupt the entire market. One of the clearest changes that stemmed from Amazon’s entry was a marked increase in customer expectations. By offering unparalleled access to delivery data, the e-commerce giant created a strong marketing improvement — a clear differentiator, since it can provide detailed delivery tracking  to customers in real-time.

There are many applications of data-driven marketing as it relates to transportation, from developing intelligence around customer sentiment and engagement information through advanced algorithms to tracking vehicles on the road to optimize routes and provide updates about load delivery timing. Here we’d like to discuss two key areas in need of improvement: shipment management and recruitment.

Marketing and operations can both benefit from effective management of shipments. Meeting  customer expectations by making sure an order is accurate and delivered to the right place and on time helps to build positive sentiment that leads to strong brand loyalty over time and repeat business, and ultimately a competitive advantage. That consistency is easily leveraged in marketing, where data can help marketers implement targeted campaigns to industry decision makers as well as potential customers who have visited their site. Beyond data, creating happy customers can further help socialize brand value  to other potential consumers through word of mouth.

As has been widely discussed, the transportation industry is facing a crisis in driver shortages. Recruitment efforts are another important example of how data-driven developments can impact performance and marketing outcomes. Companies can use marketing data in their efforts to find new drivers who display key attributes often associated with past and potentially future success. Businesses that can leverage data from past recruitment tactics and a variety of other sources have a better chance of attracting the types of drivers they need to retain, build and support the ongoing needs of the business.. Marketing to driver communities in an effective manner is especially important because it has a direct impact on recruitment, retention, utilization and revenue.

The Long-Term Benefits of a More Advanced Data and Analytics Strategy

Giving more credence to data and analytics, making changes to company culture and investing in the people and systems that boost analytical efforts can improve performance and long-term outlook. Improvement in data analytics abilities can enhance efforts to measure ROI and strategy, helping companies determine which specific actions are beneficial to overall performance and which aren’t. It aids recruiting efforts as well as contributing to improvements related to sales and revenue: As companies consider transformative changes and emphasize a commitment to improved analytics efforts, customers will recognize the value of these changes and seek to develop larger and longer term partnerships with those companies.

A reduction in customer churn is another critical consideration. Better use of data and related analytics efforts means more positive experiences, which can be marketed to new and existing clients. Market share is largely established in the transportation world, which means reducing churn and attracting customers from a competitor are top priorities.

On-time shipments and quick, effective resolutions to issues like breakdowns and delays have a major impact on customers and serve as a differentiator when they compare your business to your competitors. This is a clear marketing advantage that is derived from improved efforts related to data and analytics. Making this sort of transformational change represents a major step forward for transportation companies and will require significant effort and planning to successfully achieve. But the results of such a shift would be incredibly valuable, and would allow transportation companies to remain competitive in a rapidly changing ecosystem.

 

AI Best Practices in the Real World – Dr. Merlin Stone discusses Artificial Intelligence in the Near Future, Part II

We continue our conversation with Dr. Merlin Stone about the proliferation of artificial intelligence in the real world. Does AI’s use of algorithms always leads to smarter decisions? The answer may surprise you!

We continue our conversation with Dr. Merlin Stone about the proliferation of artificial intelligence in the real world. Does AI’s use of algorithms always lead to smarter decisions? The answer may surprise you!

Why More Mobility Is Not Always Better

Peter: We’re back again to discuss how AI can use algorithms to make smarter decisions starting with transport investment. Surely AI can be applied to help political decisions around HS2 (a high speed rail link between London and Manchester now estimated to be needing a budget of $100B+), or the third runway for Heathrow. How many times do these ideas need Parliamentary approval? These cost huge sums of money that could be better spent elsewhere such as National Health Service (NHS).

Dr. Stone: Indeed!  Don’t give the NHS any more money. It needs less money and better governing. HS2, you’re right. Did you know that HS2 doesn’t even go to Heathrow?

Peter: Last time we talked about the use of big data for people living in large cities and staying at home to be more efficient by taking a later train. That’s just time shifting isn’t it?

Dr. Stone: Yes! Time shifting is important, but there is an argument to reduce travel time to improve efficiency overall.

Peter: But if you take 20 minutes off the journey time from Manchester to London, will it really achieve anything?

Dr. Stone: Timesaving, is a different argument. The HS2 case was partly made on travel time savings, but if you identify that business people work on the train,  it’s not worth debating.  Instead put the money saved into  housing, which is a huge issue in the UK. I suggest we cut taxes and create a more efficient travel network, not another rail service.

Let’s talk about why we assume that more mobility is a good thing. There’s  the concept of ‘mobility as a service,’ which is this latest dream the civil servants have — the idea of driverless cars.  Nothing beats a bus with 40 people in it! Do you want 40 driverless cars, even if they’re all shared, clogging up our roads?

Actually, my answer to London’s congestion is to pedestrianize more because intelligence, it’s taken us a long time to do what other cities have done — it’s been on the cards for about 30 years. I think that’s a good return. And retailers have been scared of it as they don’t understand how good it is that people who feel relaxed when they’re shopping, and not worried about the amount of traffic congestion, are going to spend more … who should be told to work late in a very congested city — that requires AI.

AI in Prospect and Customer Management  … the Next Frontier

Peter: Let’s bring this back to marketing! You and I have done a lot of work on the B2B side, and I haven’t seen companies change much in their scant regard for how they store data and keep it up to date. There isn’t any data to apply AI to!

Dr. Stone:  To me, developments like LinkedIn have been very good, because they’ve forced companies to say, “Suppose there was all this data with all my buyers there, and it’s not my database, but it’s the data that’s out in the public domain that covers all the buyers that are out there in the market – How can I use that data?” Still very basic, and there’s a lot more that Microsoft could do as the owners of LinkedIn to really turn it into a fully featured B2B database.

The work done by some of our clients in terms of response management is pretty cutting edge, but too much has been done with prospecting and not customer management. It’s always a problem. I’m optimistic about that. The issue though is that this B2B stuff is so much more complex. Whereas with consumer it’s personal, maybe one or two people at most; but in B2B, it can easily be 30 or 40 people and the budget cycle could last a year or two. You’d have thought more work would have been done in this area – because of the complexity people will organize the data more and use AI more in future for sure.

But I haven’t seen that yet. I’m sure it’s on the target list for the more aware companies like Hyster-Yale; they’ve created a leading edge approach to prospect management and customer management because often most of their sales take place to existing customers. They’re two completely different things, but prospecting is more difficult due to the length of the buying cycle.

Peter: But is this something that proves marketing can really support sales, and even be perceived by the sales teams as a good thing to have?

Dr. Stone:  Exactly; because you don’t really need so much AI for consumer — if a consumer says they’re interested, then they’re interested.  Whereas a giant corporation like Amazon building a new warehouse — the number of people involved in that to grasp all the elements surely becomes a complex diagnostic problem that’s similar to diagnosing a human to see whether they’ve got cancer. So let’s say a company looking at the pattern of browsers on their website, inbound traffic on LinkedIn and a whole variety of sources can say, I can see this coming and also things like planning permissions — someone’s building a warehouse, so we need to sell them a truck that would be the equivalent, but it’s all very manual at the moment.

Manufacturing vs. Services … AI Can’t Replicate Human Touch

Peter: So there’s no linkage between systems currently?

Tracking Mobile Ad Effectiveness With Real-Time Data

The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

When analyzed effectively, this data can provide business insight on user sentiment, behavior and even physical movement patterns. Due to the sheer number of mobile devices in use, Big Data practitioners can tap mobile Big Data analytics to better understand trends across vast populations and sub-segments of users. This understanding helps business to improve engagement tactics and to optimize the delivery of services.

Mobile device data becomes particularly useful for analytics purposes when combined with extended data from outside sources. For example, weather and economic allow practitioners to correlate macro-level trends to a targeted set of users. These consumer segments have traditionally grouped users together based upon similarities. However, industry is increasingly focusing upon targeting towards individuals based upon their interests or upon their past behaviors.

Below you will find a number of ways you can apply real-time data analytics to your mobile marketing and advertising campaigns.

  • More Personalized and Targeted Ads
    Big data allows brands to better target users with more personalized interactions that drive engagement. We increasingly see ads featuring products and services we might actually want to use to make our lives better. These more personalized, more targeted ads are all based on massive amounts of personal data we constantly provide. Everywhere we go nowadays, we leave data crumbs. Following these trails reveals information about what we we’re doing, saying, liking, or sharing. Thanks to our mobile devices, this data trail now also hints at where we’re going.
  • Hyper-Localized Advertising
    The proliferation of mobile devices, primarily smartphones, has created a major opportunity for marketers to deliver contextual advertisements. These mobile-specific ads target the right people at the right time. For instance, through the combination of social data and location data, stores that shoppers are near and might be interested in can send out ads offering percentage discounts or other incentives. Delivered by shops to their shoppers in real time, these ads get consumers to walk through their doors. Hyper-localized advertising has been shown to increase customer engagement and conversion rates.
  • Leveraging Attribution to Achieve Mobile Engagement
    Leveraging Big Data about user behavior helps brands more accurately and completely quantify the effectiveness of their mobile marketing initiatives. Big data helps marketers determine whether their campaigns are creating the desired results. The ways users respond to branding assets can be used to literally create “rules of engagement” for each user or for each type of user. Marketers optimize their results through understanding varying levels of consumer engagement and through understanding the contributions of different campaigns across the path-to-purchase.
  • Real-Time Data Analytics Across the Complete Mobile Lifecycle
    In the past, conventional database solutions could be relied upon to effectively manage and analyze massively large data sets. But they did so at a snail-like pace, taking days and even weeks to perform tasks that often yielded “stale” results. By contrast, the big data analytics platforms of today can perform sophisticated processes at lightening-fast speeds, allowing for real-time analysis and insights. Shorter time to insight allows marketers to make real-time decisions and take immediate action based on fresh, reliable and relevant information.
  • Flip Traditional Consumer Profiling Upside-Down
    In the context of ubiquitous, real-time consumer data brands can now ask the data who their customers are. Contrast this to the erudite consumer profiling where consumers are targeted towards based upon their goodness of fit into an expected consumer picture. Rather than relying upon arcane consumer characteristics, instead we can now quantitatively choose targeting characteristics based upon the congruence of these characteristics with the desired call-to-action.

Brands are in desperate need for solutions that will help them understand the impact of their mobile advertising spend in the grand scheme of their broader marketing plan. This requires brands to go well beyond the click-through to be able to attribute their spend to in-store visits and better yet, sales.