Since unveiling the Unified Data Platform at NADA, I’ve gotten to talk directly to more people in automotive than any time since I’ve joined Foureyes. Hearing from CEOs, CTOs, CMOs, VPs of Sales, and VPs of Marketing about their data challenges and processes, there’s one software product mentioned more consistently than any other: Microsoft Excel.

Maybe I shouldn’t be surprised by that; Excel is a powerful tool for any business. It’s simple to start with. Pretty much every business leader is at least comfortable in it. It’s flexible to handle a variety of needs. 

Nonetheless, I am surprised that really significant and sophisticated automotive groups are using Excel to access their data, uncover insights, and track progress. 

Why Are Automotive Groups Still Relying on Excel?

My hunch is that this is due to the federated nature of many group relationships. Each store is given decision-making authority around their tech stack, vendors, and marketing budgets. So that leaves groups with, say, 20 stores, 3 different CRMs, and 14 different strategies being executed by 7 different vendors. 

Excel becomes a very attractive and approachable place to start wrangling the data to answer questions like: 

• Where am I getting my leads? 
• How am I closing them? 

This becomes problematic when you push Excel too far. Excel was never designed to be a database. And groups have a TON of data. Add these two factors together, and the solution you implemented to get to answers may be leading to false conclusions or have gaping visibility holes that you can’t even see. 

From conversations, I’m seeing 7 common symptoms to indicate that it’s time to get your data visibility and reporting out of Excel and into something better equipped to handle a large volume of data.

Symptom 1: Reliance on a “mad scientist”

In case you are your group’s mad scientist, know that I use the term with the utmost affection and respect. I’ve been this person myself:

• 2 AM Thursday: Awake because of a nagging business problem 
• 4 AM: Give up on sleep, get up, and open Excel
• 10 AM: 3 tabs, 17 hidden columns, a pivot table, and a graph that’s moving towards an insight
• 2 PM: Share with a few team members. They can’t understand the spreadsheet, but they get the graph. They like the graph. You keep improving it, and that view becomes a staple of monthly reporting. 

Or you’re NOT the mad scientist, but you have one on staff. Their graph is good, but you’re worried: 

• Maybe they’re biased.
• Maybe they’re leaving the organization. 
• Maybe they’re spending all their time in this spreadsheet. 

Whether you or someone else is the mad scientist, relying on an individual isn’t a good sign.

Symptom 2: Multiple people maintaining a spreadsheet

Excel has only gotten better at allowing people to collaborate in a single document, and reliance on a single individual has its drawbacks. So multiple people *shouldn’t* be an issue. And yet, multiple people maintaining a spreadsheet is a symptom of a problem because Excel is not designed to be a database. When you start using Excel as a database and invite multiple people to manage it, you open yourself up for errors. 

Some common errors that you start to see: 

• Row count limitations
• Duplicate records
• Empty fields
• Formatting that conveys information

Symptom 3: A really costly mistake

Data errors like the ones outlined can sound trivial, like minor annoyances that really precise people get hung up on. But once you start using data to make decisions, data errors can lead to costly mistakes. I stumbled on this page that collects spreadsheet mistakes that hit the news. Lost business opportunities. Paying wrong salaries. Inaccurate electoral votes. It shows the variety and relative frequency of active mistakes coming from pushing Excel beyond its intended scope. But I think the pernicious problems are the ones that escape us because we don’t have clean data. For example, how much money do you think the average automotive group spends unnecessarily because of an inability to answer where they are getting their leads?

Symptom 4: Excel running slow

More than any of the other symptoms, Microsoft running slow is the most objective and easy to spot sign that you’re relying too heavily on Excel. 

Symptom 5: Data access limited to a developer

A lot of groups have told me about their efforts to get data out of their CRMs. In addition to political hurdles when CRM companies exert ownership of the data input by dealerships into their own CRM instances, there are technical hurdles as well. Many CRMs use APIs or file transfers that are really only accessible to people with coding skills. I talked with one group who negotiated for over a year to get their data from the CRM vendor only to be provided with an API that required a developer’s skills to use--and when they got a developer hired, they found that the API only had a portion of the data they needed. 

While this isn’t a failing of Excel directly, it is indicative of how Excel is too simple of a software to match the complexity of the data challenge facing automotive groups. If the data is too hard for a typical businessperson to access, then the tools of the typical businessperson are unlikely to be the right tools for the task.

Symptom 6: Three or more sources

To combine data successfully, you have to normalize the data so it matches up. Take for example something as simple as a lead generator’s name, like TrueCar. Or is it True Car? How about TrueCar.com? Or True car? In our work normalizing automotive data, we’ve seen more than 300 variations of this one lead source in a single group’s dataset. 

Additionally, you need to understand the data and the decisions being made to get to the numbers. Take something as simple as open rate on an email. It’s easy to assume that every email tool uses the same formula, but they don’t. Here are three variations I’ve encountered: 

• Total unique opens/total sends
• Total unique opens/(total sends - total bounces)
• (Total unique opens - opens triggered by system caching)/(total sends - total bounces). 

All three formulas are reasonable, but If you have two vendors using two different formulas, you are going to need to get more data to normalize the open rate.

Symptom 7: Lack of clarity

Maybe the most meaningful symptom of all is that you feel unanchored to your group’s performance. You’re consistently frustrated that you can’t quickly see what’s making one store excellent while another is average, and getting apples-to-apples data feels elusive. 

If that’s where you’re finding yourself, know that you’re not alone. I’ve talked to massive groups, really smart groups, and some of the highest-performing groups in the industry and all of them are feeling this pain.

Can Foureyes Help?

Foureyes has spent the last five years wrangling automotive data. Our developers integrated with all the CRMs, engineered a simple script to track all forms, calls, and chats on your website, and collected all the franchised inventory on a daily basis. Our data scientists have explored, normalized, and visualized the data. Now we’re making all of that data accessible to groups through the Unified Data Platform. My hunch is that it can automate and scale a lot of what you may be managing in Excel. Just get in touch.

Big data has been promised as a transformative solution for decades now. The promise of big data was always that it would allow you to answer all your questions and get to the bottom of things. The data derived from the internet was supposed to answer the age-old adage of “Half the money I spend on advertising is wasted; the trouble is, I don't know which half.”

Yet, here we stand—swimming in data, but as lost in it as we ever have been.  

The Internet Killed “The Pulse”

Retail environments are pulse-driven. An experienced leader used to be in the store, feel that pulse, and know how things are going. You could see that you didn’t have enough traffic to hit your month’s sales target. You could hear the conversations and understand things were pacing in the right direction. Now, that feeling isn’t as strong. Website shopping isn’t as visible as people on lots. Hundreds of text and form leads are handled without anyone saying a word. The process is absolutely more convenient for customers, but we’ve muted the pulse. Add to that the number of stores being overseen by groups who only have an occasional in-person presence, and the gap feels massive. 

Data was the promise. It was supposed to bridge the gap created. It was promised to be more objective and there would be enough of it to answer all the questions. But there are few small- to mid-sized businesses today, whether in or out of automotive, that have mastery over: 

• Capturing the right information
• Developing insights from their data 
• Activating their data in a transformative way

So why hasn’t it happened? What gets in the way of an average-sized business realizing the problems of big data? Big data is a big topic, so let’s focus on automotive and break down the problems to more easily identify a path forward. 

As I see it, there are 5 major issues:

1. Too much data
2. Data that is not actionable
3. Locked, siloed systems
4. Dirty data
5. Reliance on vendor reporting

The Five Broken Promises of Data

Problem 1: Too Much Data

Every retail business today is swimming in data. Every system you use and every piece of software you run produces—and likely reports on—a massive amount of data. Simply starting a list of potential data sources makes it feel daunting to get a handle on the problem, not to mention the fields. Your CRM. Your website. Your DMS. Your email automation. Your advertising platforms. Your call tracking. Everything is producing data, and it’s hard to see what’s going on when you are swimming in so many data pools. 

Problem 2: Data That Is Not Actionable

Most of the data being produced is not actionable. You can’t ask additional questions or gain conviction on opportunities to chase. 

The standard of “actionable” can be hard for many people to evaluate. For determining actionable data, we use the framework that actionable data should enable you to ask better questions—not to answer questions. Marketers and business leaders should be able to look at a dataset, ask one question and then another better question to gain conviction on the problem to solve and the opportunity to chase.

For example: 

Where do my leads come from? → 

• OK, then what sources are generating the leads? → 
• OK, then what sources are generating the sales? → 
• OK, then what is leading to the close rate difference of Source A vs Source B? Is it a lead quality, people, or process problem?

This is how actionable data functions, and by contrast, most companies’ data is filled with data deadends and information that, while interesting, lacks an ability to move to action or follow a discernable thread. 

Problem 3: Siloed, Locked Systems

Each of the systems producing data is siloed (meaning they make it hard to play well with other systems) and locked (meaning it’s hard for you to get the data out of the system to play with directly). 

My personal belief is that systems are siloed because automotive vendors have historically seen data as a revenue source and/or competitive advantage versus truly believing the dealer owns their data. They’ve put up a wall and charged substantial access fees to other software providers that are passed along to dealers. While this has been normalized over the past decades, it’s an incredibly dated approach. Compare it to Salesforce and the partner ecosystem they have built where thousands of apps can leverage their data at reasonable costs. It’s what’s made Salesforce so dominant, yet no one has taken that stance in automotive. 

Some dealers and groups have asserted ownership of their data and worked with providers to get access to the data. But in every instance I’ve seen, the providers give clumpy access that has gaps. Some CRMs are still doing “data exports” that are unreliable and missing information or providing APIs that are missing information. Most dealer groups do not have the technical expertise to collect, clean, and troubleshoot the disparate systems. Oftentimes, the marketer or marketing teams become the de facto data people, but most marketers don’t have the time or skills to manage it at scale. Maybe they can do it once or twice and work in an Excel spreadsheet, but ongoing maintenance and easy access to good, actionable data becomes elusive.  

Problem 4: Dirty Data

The moment you start working with data is the moment you understand how dirty it really is. Take something as simple as a lead source like TrueCar. Or True Car? TrueCar.com? True car? In our work normalizing automotive data, we’ve seen more than 300 variations of this one lead source in a single group’s dataset. So there’s a ton of normalization work required, especially around lead sources and inventory.

But there’s also incorrect data. Most vendors who submit leads (via ADF) name themselves as the lead source. In doing so, website widgets such as chat and forms get overstated credit and cause confusion as to what the actual media source that delivered the lead is. We have consistently found that for website leads in dealership CRMs lead sources can be over 70% incorrect.  

Problem 5: Reliance on Vendor Reporting

The majority of automotive retail data today is supplied in the form of vendor and platform reporting. You know how many leads were generated by Facebook and Google Ads because your digital advertising vendor tells you that information. You know how many visits your website generated because Google Analytics tells you. 

The issue is that none of the data matches up, vendor-to-vendor or platform-to-platform. Maybe it’s accurate or maybe it’s slanted to put them in the best light. But Google Analytics is different from your marketing automation platform which is different from your CRM. It doesn’t matter if you are counting visits or conversions, and it’s a massive headache to figure out what or who to trust, putting you in a position to question everything. If you’re like most dealerships, in a month that your sales team was working 100 leads and made 30 sales, your vendor reports all added up would show 300 leads and any one vendor would claim credit for as many as all of those sales. It’s reached a point where many automotive stores and groups struggle to hold vendors accountable or believe the information being served to them.

So Where Do We Go From Here?

My belief is that dealerships and automotive groups won’t get out of the current data quagmire until they own their own data. And ownership means that you have possession of it. You can see it. You can touch it. You can do stuff with it.

This belief that progress begins with data ownership is the major driver behind the latest Foureyes innovation, the Unified Data Platform. If you’re curious to understand more of what that is and how it could work for your group or store, check out additional details about the Unified Data Platform here or get in touch. I’d love to show you what Foureyes has done to truly make it simple for you to start reaping the benefits of big data. 

Since the fall of 2021, Apple Mail Privacy Protection updates have been changing the email  marketing game.  

We see the impact of these changes on brands who rely on email to connect with prospects and  customers. We also see it first-hand as a partner for automotive dealers who use our Prospect  Engagement email marketing tool. As such, we feel compelled to help you understand what  these changes mean for your dealership. 

What’s Changed? 

The Apple MPP feature intentionally hides email data and according to Apple, “helps protect  your privacy by preventing email senders from learning information about your Mail activity.” As  a result of this change, senders may see up to a 100% open rate from Apple Mail users  regardless of whether the individual actually opened the email.  

Apple’s release had a staggered launch, but there is a clear change in reported open rates – and an inverse effect on click-to-open rates – starting in fall 2021. We’ve seen the effects of this  change in our own data per the graphic below. However, not all metrics are affected, including  the click and click rate. 

What Does This Mean For Dealers? 

With approximately 52 percent of all email opens happening on Apple devices, this change has  a significant impact on email marketing data and reporting.

While the email marketing  community has been grappling with these changes since they were initially announced, this  update has flown under the radar for those managing dealership emails. 

Open Rates Are Out, Click Rates Are In 

Email metrics enable you to make one-to-one comparisons. You can use them to evaluate  campaigns, vendors, tools, and A/B tests. 

But open rates, a long-standing, primary measure of success for many brands, has suddenly  become a largely unreliable and inflated metric since the release of Apple MPP. Similarly, the  click-to-open rate (CTOR) metric also suffers from the same issues since it’s measuring the  action of clicks against opens.  

In the wake of the update, many across the email marketing space suggest that clicks and  click rates should be the new de facto standard. Email click rates remain accurate despite  Apple’s changes, and have already been a key performance indicator for email marketing. 

They’re arguably a more reliable and insightful metric given they: 

- Reflect your email’s overall performance on topic, messaging, creative, subject line, etc.

- Tell you exactly what recipients have an interest in and be especially insightful if you  have multiple CTAs

Vendors Must Lead the Way 

Adjusting your reporting metrics for success is an important step, but we believe the real story  around Apple MPP for the auto industry is less about numbers and more about vendor  relationships. 

As a vendor in the email marketing space, we want to partner with our customers to navigate  these changes. Too many vendors have swept the effect of the Apple changes under the rug, or  worse, taken credit for the boost in open rate. 

If your vendors–email providers, agencies, or other email partners–aren’t bringing these  changes up to you, they’re doing you a disservice. 

Data transparency relies on all vendors proactively working to provide reliable and accurate  data. Beyond acknowledging known inaccuracies, we believe this promise includes openly  discussing how external factors may be impacting your information, even if, and especially  when, you may not be aware. 

While we don’t claim to have all the answers, we do want to have open conversations with our  customers to help them solve current challenges. 

Where Do We Go From Here? 

We know email opens are still a primary reporting metric for many dealers. While we still  encourage dealers to consider shifting to other metrics like click rate, we know it’s unreasonable  to do so overnight.  

We believe in transparency and working alongside our customers as we navigate industry  changes together. So to help, we are making changes of our own. 

Filtering Out Email Opens From Bots 

In response to these changes and to give more options for our customers, our email tool,  Prospect Engagement, now lets users exclude all email bot activity – extending beyond just  Apple email and device users. This gives you a clean view of actual human email opens for  more accurate analysis, and in turn actually increases your click rate and click-to-open rate as a  result.

Changes You Can Make Today 

1) Ensure everyone who sees open rate as a measure of success is aware of these  changes. The first step is awareness, and whether you share email results in your  monthly reporting or in vendor conversations, add this as a key update.  

2) Focus on click rate. Shift to this metric as a default measurement when making 1:1  comparisons, particularly when making comparisons before and after the updates were  released. 

3) Ask questions of your vendors. While we hope they lead the conversation, if you're not  getting the information you need, ask. We don’t expect perfection, but they should have  insight into the changes they are (or aren’t) making in light of Apple MPP.  

Whether you’re a Foureyes customer or not, if you use email marketing for your business, these  changes affect you. While change always has some hurdles, this is also an opportunity to  connect with your partners and vendors on your priorities, see who is committed to data  transparency, and improve your email program as a whole. 

It’s no secret that we love data at Foureyes. We use it to track industry inventory, measure consumer buying behaviors, and power our products 

With data, we also find stories. In this case, we’ve captured a list of data points at different stages of a prospect’s buying experience. The intent is to spotlight how prospects behave while they shop and how you–the dealership–can accommodate or adjust your sales process to improve the prospect experience and ultimately reach your own goals. 

Here’s what we found: 

Prospects start online… often. 

1. 92% of car buyers research online before they buy (Google). 

2. 83% of auto shoppers intend to do more background research on potential cars online than before (Capital One). 

But often, their experience doesn’t get off to a great start. 

3. 41.2% of the average dealership’s qualified leads are “mishandled,” meaning calls were missed, follow-up was delayed, or lead inquiries weren’t logged to the CRM (Foureyes). 4. 6.5% of calls were missed (Foureyes). 

5. 11.7% of leads go unlogged (Foureyes). 

It takes a lot to get people in the door or become a lead. 

6. The average cost of dealership advertising per new unit sold is $541 (NADA).

But even once they become a lead, they continue to research…

7. About half of the active qualified leads on dealer websites are returning visitors who previously called, chatted, or filled out a form via the website (Foureyes). 8. The average auto shopper visits 1.5 dealerships (Google). 

…and their experience is a mixed bag, depending on your own personal benchmarks and priorities: 

9. Qualified sales calls experience a 74-second median hold time (Foureyes).

10. Nearly two-thirds of qualified leads who return to a dealer website experience delayed follow-up…and 60.9% of this group didn’t receive any follow-up after one week (if at all) (Foureyes).

But once they start engaging, your own first-party data can help make it clearer who to prioritize, and when. 

11. Leads who end up buying view 10.3 vehicle detail pages on average before doing so (Foureyes). 

12. 58% or more of website leads who buy do so within three days (Foureyes). 13. 34.3% of leads who buy come to your site organically. To put it in perspective, only 19% of leads from paid channels and 1.4% from social end up buying (Foureyes). 

Data + your people, processes, and technology 

A prospect’s experience usually boils down to how dealers use people, processes, and technology to engage and nurture leads. Good data provides answers and guides decisions in these areas. Great data however, lets you ask even better questions. Consider that for a moment when you’re deciding which leads in your CRM to prioritize today… Why are you going to contact them, and how will you approach it? 

We like to think we’ve cracked that nut at Foureyes by giving dealerships the ability to enrich their conversations and personalize their outreach using their own lead data (e.g. tracking leads as they shop your site and prioritizing the more qualified). Want to see how? Get the rundown here!

To start the conversation about today’s state of data in the auto industry, I want to start by talking about Moneyball. 

Yes, I’m talking about the 2003 book turned Brad Pitt film chronicling the story of Oakland A’s general manager Billy Beane. 

As the story goes, Beane manages the team through a difficult loss, and the team is losing star players. The A’s budget is limited, and rebuilding the team seems unattainable. Facing these challenges, Beane meets Yale economics grad Peter Brand. Brand has new ideas about how to evaluate players and uses the data available to better predict a player’s chances of success. 

The rest is a storybook ending. Brand joins as the assistant general manager, and soon he and Beane are using data and their new methodology to find the undervalued players, and the A’s go on to win the American League West title. 

What Does This Have to Do With Automotive Data? 

Moneyball is one of the first widely-viewed examples of data as an opportunity. This opportunity is much larger than baseball or any one industry, and has become an assumed fundamental business belief. If you can figure out how to use your data, you have an advantage. 

Prior to the 2000’s, data was expensive. As it became cheaper to store data, businesses were told to collect everything, and so we all started to hoard it. Like a stack of newspapers from 1998, we didn’t know why we needed it, but we wanted to keep it just in case. 

This shift takes us to today, where we spend an unbelievable amount of time and money on data. We continue to collect immense amounts of data. And by collecting so much data from so many different sources, we’ve created the problems that we’re now trying to fix. Worse yet, we still can’t answer the basic questions. 

Why Automotive Data Is Especially Tricky 

While these challenges are true for all industries, there are three key reasons why using and analyzing data is particularly challenging for the automotive industry. 

1) Auto is federated. Dealers have some autonomy, but still must report up to the dealer group and OEM. So while the freedom to make those decisions can be a distinct benefit,

it also means that two dealers within a group could have different CRMs, marketing strategies, tech stacks, and sales processes. This poses a challenge when groups want to analyze success. 

2) Auto is legislated. It’s true that the automotive industry is heavily regulated–you only need to take a look at the news to see how quickly the rules change. But vendors use the changing legislation as an excuse to separate dealers from their data. They deny access with the excuse that they’re only protecting dealers. This is not a means of protection, and there is no reason you shouldn’t have access to your own data. 

3) Auto is siloed. Right now, any given dealer has data from these four systems: inventory, website, CRM, and DMS. They don’t talk to each other well, making full-picture data analysis challenging. 

Considerations to Get the Most From Your Data 

Improving automotive data is complicated and will only happen through collaboration between all parties, from vendor to dealer. Here are the guardrails that we believe can make meaningful change happen: 

1) Own your expertise. Vendors are helpful and necessary for data visualization and collection, but they don’t have the skills and insight you have into your own business. I like to say that good data doesn’t give you the answers, it helps you ask better questions. You have to combine good data with your context to get real answers. We can help you measure walk-in traffic, but only you know that there’s been a construction project for the last two weeks that’s affecting your traffic. 

2) Demand more from your silos. Everything you put into a system should be able to be extracted. If that’s not the case, your vendor is basically saying that they own YOUR data. 

3) Question the black box. If a vendor is unable to explain to you how they provide the numbers or answers that they have, then it is too good to be true. Everyone wants to deliver the answers, but not everyone can back up those answers with methodology and technical authority. 

“If I was so good at selling cars, I would actually probably start my own dealership.” -David Steinberg, Foureyes Founder & CEO 

What’s Our Answer? 

Foureyes views these challenges as an opportunity for auto groups. We’re not satisfied seeing these issues and ignoring them, which is why we built a tool that directly addresses these hurdles.

Our answer is the Unified Data Platform (UDP). The UDP unlocks the mountain of data you and your dealers are sitting on today – across website, inventory, and CRM data – and compiles it into a single dashboard so you can better report, optimize, manage, and make more informed marketing and sales decisions within and across rooftops. You can use it to import and own your data while also having access to pre-built and customizable reports. Combined with your context, the UDP enables you to tackle the questions that drive businesses today, like: 

- Where are we getting leads from? 

- How are we closing our leads? 

Our latest offering is also our most ambitious, which makes it all the more exciting to share, and we even have a sneak peek into the tool here. If you or your group is interested in accessing the data needed to fuel those better questions, contact us today.