data.
In the modern world, business decisions cannot be made on acumen alone. We need data to drive decision-making when it comes to many facets of business including, but not limited to, purchasing, expansion, advertising, marketing campaigns, selecting vendors and budgeting.
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Return on Ad Spend (ROAS)
Understanding Return on Ad Spend (ROAS) will make or break the success of your ads campaigns. Put plainly, if you spend more on your advertising than you make on it, then it's time to make a change.
For example, if your spend $5,000 on ads, and you can attribute $8,000 of revenue to that ad campaign, your ROAS would be 160%. This may sound good, but it's really not. Any ROAS score under 200% is considered under performing. In fact, it's not considered successful until your ROAS is at least 400%.
ROAS calculations are a great way to make informed decisions about your advertising strategy because it gives you quantitative data on the performance of your campaigns. If you are running several campaigns at once, which you should, regular ROAS calculations will guide you and your department toward the most effective routes for those precious marketing dollars.
Special note: ROAS vs ROI : ROAS tells you how much money your ads bring in, while ROI informs you about how much profit your company earns.
Google Analytics (GA4)
Google Analytics (GA4) is a beast, but in a good way! This software allows you to peek behind the curtain to learn more about your website visitors (online behavior, geographics, how they access your website), which pages on your website are getting the most attention, how users engage with your website, and so much more.
Within GA4, there is the mighty Tag Manager (or GTM for short). This allows you to link your website to Google Analytics so it can start collecting data on your visitors. This is done by placing a line of code in the header section of any page(s) you want to collect data from. All web builders either have this integration readily available or through a free plug in. Once this is activated, your Google Analytics dashboard will start collecting data. There will be about a 2 hour lag between data collection and it appearing on your dashboard. Data collected here can be formatted into reports based on the variables of your choice and used to learn more about your leads so you can target them more effectively, as well as make informed decisions about the layout and usability of your website as a whole. Below is an example of what your Google Analytics dashboard would look like.
GA4 isn't just important to optimize website performance, it can also be used for mobile apps too. The overall functionality is very similar and you can get the same types of reporting to help better inform your business decision making.
With Google Analytics, you will also have access to a fantastic keyword library built off of the terms Google users around the world use everyday. Utilizing this engine can help you make your website more searchable and rank higher in Google searches. This is the same library found in their sister application, Google Ads.
Customer Profiles
I may be aging myself a bit here, but do you remember library check out cards? It was a heavy stock paper card that you had to sign and date and stick in the pocket in the back of a library book before you could check it out. I remember looking at all the names and dates - the colors of the ink used, the penmanship, perhaps even see names repeated - did you ever wonder about those people and what you might have in common with them? In a way, this is how customer profiles work.
A customer profile is a collection of data points personified. It is a way for your marketing team to create a snap shot of who your company wants to have as a customer. For example, if you are selling high end sports cars you are going to want to target consumers who are sales qualified and match certain demographics, psychographics, and purchasing habits. Your customer profiles, just like all the names on the check out card, should all have at least one thing in common. In this case, being in the market for a high end sports car.
Above are three very basic customer profiles. Of these three, which do you think would most likely be someone to buy a high-end sports car? James, of course. Sure, Colleen may be sales qualified, but women with children are not the target market for sports cars, and Jake might really want one, but he is year away from being able to afford it. If you dive deeper into building a profile for James, you'll be able to tailor your marketed efforts and place your ads and promotions so he, and others like him, will likely find them.
Excel
I am experienced in building reports using this great and powerful tool. From the handy "=SUM" to building complex and customized formulas for segmenting data under any combination of variables (including pivot tables)- I have it covered. Data is always looking out for our best interests, and it's just a matter of understanding how to make it talk.
Mock Sales Report
Below is some mock data encapsulating a fictional sales team's current prospecting efforts. It includes data points such as names, addresses, city/state/zip, contact information, service type, opportunity size, name of the sales rep, and date of first contact.
Click the chart to enlarge
For columns titled 'Service Type', 'Opportunity Size', and 'Rep Name' I used Excel's data validation function to create drop down menus to be selected from and built the possible options into them. Using data validation in spreadsheets like this one prevents against transcription error and forces users to select from pre-determined options so formatting remains consistent. This allows people like me to synthesize the data more quickly and efficiently.
Let's take it a step further...
As a sales manager, you want to be able to know how your team is doing without having to pour over raw data like this.
For sake of argument, lets say you want the following answers at a glance:
1. How many leads are each rep working on?
Answer: =COUNTIF(J4:J14,"[name of rep]")
James - 4 leads
Michael - 3 leads
Sarah - 4 leads
2. Which service type is more commonly sold?
Answer=COUNTIF(H4:H14,
"Residential")
Residential - 6 accounts
Commercial - 5 accounts
3. Who has the fasted sales cycle?
Answer: First determine the individual sales cycles with =DATEDIF
=DATEDIF(K4,L4,"D")
K4 is the first active cell in column "First Contact Date", and L4 is the first active cell in column "Close Date", and "D" indicates we want to return the number of days between the two dates. After making this formula apply down all the rows,
Second, apply =AVERAGEIF
=AVERAGEIF(J4:J14,"[name of rep]",M4:M14)
This will return the average sales cycle for each rep:
James - 6 days
Michael - 14.5 days
Sarah - 8.33 days