Today on the Demand Gen Pod, Episode 16, Ryan discusses the importance of data analysis in marketing and how it can inform decision making. He emphasizes the significance of collecting accurate and high-quality data through protocols, validation techniques, and regular audits. Ryan also mentions the need to consider consumer lifecycle data and utilize CRM tools to streamline data collection. Setting goals and KPIs upfront is essential for effective campaign building, as it provides a framework for measuring success. He advises aligning metrics with marketing objectives and using data visualization tools to present insights clearly. Lastly, Ryan teases an upcoming episode on industry benchmarks and their evolving meaning.
Summary notes from Episode 16:
Ryan introduces the topic of data analysis and its importance in marketing
decision making. Ryan asks for subscriptions on YouTube and TikTok. Good data
collection is crucial for successful analysis. Implementing data collection
protocols, such as using drop-down menus for country selection, can improve
data quality. Data accuracy and completeness can be enhanced through data
cleansing tools and regular data quality checks. Tracking and managing email
data, including automated responses for individuals who have left a company,
is important. CRM tools and data management platforms can streamline data
collection and improve data quality. Defining goals and KPIs upfront is
essential for effective data analysis. Industry benchmarks can be used to
assess performance and compare against industry standards. Choosing the right
metrics involves considering relevance, data availability, measurability,
accuracy, and alignment with business goals.
Full Transcript:
00:00
Welcome to the demand gen pod. My name is Ryan. Today we are talking about data analysis and how we can use it to inform some really solid decision making over in marketing. So let’s get started. Before I forget, if you could, I would really appreciate a quick subscribe, especially if you’re watching us over on YouTube or we’re over on TikTok and all that fun stuff as well. But listen, let’s get into it. So data analysis, obviously it informs marketing strategies and decision making simply by providing some valuable insights and understanding on customer behavior, their trends, their preferences. And successful data analysis. And marketing really helps businesses optimize campaigns, personalize marketing efforts, and better achieve results. And if you listen to this podcast, you probably hear a lot of similar words, personalize, optimize opportunities, all of those things.
00:58
We really talk about that a lot, and that’s because, I mean, it’s really the core of it, but it’s so important. It all starts at data collection. Bad data, bad analysis, good data, good analysis. Well, I guess I should say good data potential for good analysis. It depends on how well you do the analysis, right? Best practices for collecting and ensuring that data quality could include things like implementing data collection protocols, leveraging data validation techniques, and then regularly auditing data sources. We have an episode of that too. What do I mean by that though? What is implementing data collection protocol? I’ll give you a really easy example. If on your form you require something like country, provide that as a drop down, don’t provide it as an open text field. So some people will put in us, other people within USA, United States.
01:48
United States of America, right. And this is true for all sorts of countries that you could get various data for. Also, the other thing you can do is that if you do need to collect country, maybe you need to collect country because you have other business units that operate outside of the US and you only operate inside of the US, but still people from other countries come to the primary website. This is definitely true with larger companies. You might want to be collecting country to make sure that you push those leads over to your appropriate country, or it might even go to a different CRM entirely. And if that’s the case, but your vast majority of conversions are coming through the United States, then make sure that first country is preset to United States so you’re not making people do extra work.
02:33
Because then if you do that for them, you’re far more likely to have them get it right and choose United States rather than it starting on, say, Afghanistan or wherever. Kind of the first country is, I think, in the list, and then United States is three quarters of the way down the way. So certainly I think that will lead some people to simply say, I don’t care, I’m from this country, even though they’re not from that country, they’re actually from the US. What else? So the other thing that you can consider is that you need to leverage things like the data accuracy and completeness and then the reliability by using some data cleansing tools and then conducting regular data quality checks, which we have an entire episode on data audits and validating data against benchmarks. And that can be really helpful too.
03:23
The other thing that you need to keep in mind about data is that I think that consumer lifecycle data, actually b to b lifecycle data, I think, has a lifespan of about three months. So basically, people move jobs all the time. And so if you’re tracking emails and conversions that you’ve had, the ods of them having left can be pretty good, especially if you’re not collecting it particularly often. So you want to keep up with that. You can also have somebody in charge of the return email, the reply to, because if somebody has left a company, the vast majority of the time what they end up doing is having some sort of auto reply saying, I’ve left my new email is this, or I’ve left, contact this person instead. Then you might want to be able to have somebody manually go in.
04:11
Or you could also certainly potentially automate something like that. Might not be super reliable just because of all the various inputs that you could be receiving for it, but you could maybe potentially automate something like that to be able to go and update your CRM as well. Speaking of CRM tools like CRM systems and data management platforms and data governance tools, they can all streamline data collection and improve data quality. And so if you’re using something like Salesforce, it’s important to remember that they have lots of these tools built in as well. Also alongside preparing and looking toward opportunities with improving your campaigns and improving it through data analysis are defining your goals and defining your KPIs. So looking at a gigantic spreadsheet of data and making something of it can be really difficult without having your goals and your KPIs upfront.
05:12
And when we build campaigns, we build them in a way which is explicitly designed around KPIs. What do you want out of it? What’s the point of sending these emails? What are we trying to get somebody to do? Is it simply to just be aware of what we offer? Or is it actually to follow some sort of conversion funnel and move them through a pipeline. If that’s the case, what are the key points of the pipeline? What are we going to measure? How do we know that somebody has reached that key point? And then not only how do we know, but how do we track it, and then what do we do with that information?
05:48
And then as far as the KPIs are concerned, put numbers to those items and then hold off and try to understand how you can actually better leverage it for future campaigns. So something to certainly consider. The other thing that’s really good about that is to consider those industry benchmarks and then keep in mind, how are you doing against the industry? One thing to note on that, though, is that I think that you’ll find benchmarks are exactly that. They are generalized topics and numbers right across vast amounts of data. And I guess that does give you an average. I mean, that’s exactly what a benchmark is. But there are different ways to collect benchmarks. Let’s say, for example, you are trying to collect benchmarks on open and click through data for your campaigns. Okay?
06:37
So if you’re looking to do that, what you can do is you can look at not only not just your leads or customers as a whole, but you could break them out into smaller cohorts. So if you have leads that are cold, let’s say you haven’t touched them too much, but you want to warm them up, and then you have leads that are warm, well, you might want to be looking at open rate benchmarks and click through benchmarks and what other, the other KPIs that you’re defining upfront and early, you might want to look at those and split them out between those two cohorts so that you can see that open rate for cold leads might be three 4%. Open rates for warm leads might be 15%. Open rates for customers might benchmark around 20%, 22, 23%.
07:16
So if you know that’s a very different figure than taking all those numbers and averaging them together and then saying, well, we’re never hitting that on the cold lead side and we’re way exceeding it over on the customer side, well, that’s not really true. You’re just grouping them all together and getting that average. And that can be a really tough spot for you to put yourself into because you may not actually recognize when you’re doing better or when you’re doing worse. So other factors to consider can include your business objectives, which may come from higher up your target audience, budget constraints, and then timeframes as well. So a really well defined goal and KPI. They can both contribute to focused and meaningful data analysis by providing a framework for measuring success and progress. And that leads us to choosing the right metrics.
08:01
So in choosing the right metrics, some considerations that you should have are selecting metrics that include relevance to your actual marketing objectives, the availability of the data that you actually have. So I mean, it’s tough to make decisions or goals around metrics that you don’t realistically have access to or that you don’t have widespread access to. How measurable are they and accurate are they? And then the alignment of those to actual business goals. So also some things to consider, and then businesses can align those metrics with marketing objectives by linking them specifically to different marketing tactics and strategies.
08:37
So like I was saying, if the goal is to grow revenue with a campaign, then to be linking that goal to marketing tactics and strategies and emails and programs and campaigns and retargeting structures that are designed with the one focus in mind to push people through the funnel. So that’s kind of what I mean by that versus more like a top of mind brand centric program which might not necessarily need a whole bunch of links to get people to go to the website to get them to convert. It might just be simply a matter of helpful tips or whatever the case may be. And then some examples of key metrics in different marketing channels like social media, engagement rate, click through rate, email you have open rate and conversion rate, and then in website analytics you have things like bounce rate and conversion rate.
09:23
So I hope this has been helpful. Just some things to at least get you started. And as you work through it, you can also leverage some really cool data visualization tools and that can translate this data into things like charts and graphs. And it can present data insights rather in a really clear and concise manner. And so that can help you to really understand what you’re looking at, because you’re looking at a visual way you can apply different color schemes and that can reduce clutter and also provide additional context that you may not see if you’re just staring at a table. It’s also particularly helpful for stakeholders and higher up business teams to be able to look at charts and graphs to really quickly understand vast amounts of data.
10:09
With that said, we will see you next week where we are going to be talking about some industry benchmarks and specifically how they don’t mean what they used to. So I hope that you join us for that episode. Until then, my name is Ryan, this is demand Gen Pod. You can reach out to us at hello@demandgempod.com. Thanks for listening.