• Dan White

ASSESSING PPC POTENTIAL USING GOOGLE KEYWORD PLANNER


Launching a new Google Ads campaign for your newest product can seem like a no-brainer. Yet, time and again, the up front analysis of whether or not a campaign will be effective (or even if it’s needed) is sorely overlooked.


This post changes that.


What I’m going to run through in this post is the exact process you can run through - for free - to establish up front if you can launch a profitable Google Ads campaign.


The data you have from Google Keyword Planner helps you figure this out. That way you know up front:

  • How many people are in the market for your product

  • What the costs are for driving people to your website via PPC

  • What the CPA is going to look like for selling your product

The data that comes back can be incredibly revealing. I’ve used this to show that the budgets just aren’t big enough to reach targets. There’s been other times when it shows there’s untapped potential in a campaign to be immensely profitable. Either way it’s better to know this up front.


If you want to follow along and conduct your own market assessment for a new product or service here’s what you’re going to need:


WHAT YOU NEED TO ASSESS PPC POTENTIAL:

  • Knowledge of a product, its price and its USP’s

  • An understanding of your audience and the country they’re based in

  • Access to Google Keyword Planner*

  • Google Analytics Conversion Data (optional)

  • A spreadsheet

  • Google Trends

*This process only works if you have accurate keyword volume data. Without a Google Ads account that’s up and running the search volume figures will be generalised and not useful enough for what we’re planning. If that’s the case then either start an Ad campaign or use the keyword planners available in other SEO tools.


To run through this process we’re going to need an example product. So I’m going to pick men’s pyjamas - with a mid-market price range for folks in the UK. Because, why not? Would Google Ads be a profitable channel to use to generate sales?


Let’s begin. Follow along using the Google Sheet here:


1) KEYWORD RESEARCH

  • Head over to Google Keyword Planner

  • Adjust the settings to only include your target country. It’s best to only target 1 country each time. (If you’re planning on targeting multiple countries it’s best to repeat this process as the CPC, demand and seasonality can vary greatly. Merging the data from multiple countries can blur the results)

  • Make sure the date range is set to the last 12 months

  • Start with broad phrases and narrow down accordingly. Add absolutely every relevant phrase you can find to your keyword list

  • Include branded keywords - both your own brand terms and competitors

  • Only include phrases which have a high commercial relevance. e.g ‘mens silk pyjamas’ rather than ‘when were pyjamas invented

  • Only include phrases which you can legitmately satisfy the user’s intent. e.g don’t include ‘mens silk pyjamas’ if you only sell cotton pyjamas

1.1) DATA CLEANING

  • Export your data into a spreadsheet so you have your raw data. Keep everything but the competition fields. Definitely don’t delete the average monthly searches broken down by month

  • Manually work through the list and assign any any brand terms with a note/colour. We’ll be splitting these out from the bulk of the keyword data. If you’re an established brand then you can split this out again seperating your own brand from your competitiors.

  • Once done you should be left with with 3 datasets - generic searches, competitors and optionally your branded search terms

  • If you want to you can split the generic searches into more granular batches of keywords say by product type, fabric or style. For the moment though we’re going to keep our keywords into one single list

  • Starting with your generic searches you’re going to want to calculate x2 key figures:

  • The total average monthly searches

  • The average figure taken from the the top of page bid (low range) and the top of page bid (high range)

  • This is going to form the starting figures needed to calculate profitability on your product

  • Here’s what the data looks like when tidied up:

2) PLANNING THE FUNNEL


This is where it gets interesting. At this stage you now have the figures for the annual demand of your product and the estimated average CPC to get someone to the website. Next we need to factor in your CTR.


2.1) FACTORING IN CTR

The CTR of a campaign is one of the two critical figures which you have control over. The more attractive your ads the more people will click and the greater the marketshare you can tap into.

  • CTR’s can vary hugely. Perhaps you’re already running campaigns and know a historic CTR value you can benchmark against. If you don’t then CTR’s can be anywhere from 1.5% to 10% depending on the optimisation of campaigns.

  • Add these figures into the orange coloured fields as you can play around with it to see how it affects the final results. Different campaigns will also perform differently. Brand campaigns will have a higher CTR than a competitor campaign for example.

  • With the pyjama market we know that there’s an overall demand of 108,140 monthly searches for generic pyjamas

  • Let’s include an average CTR of 4%.

  • That means in the month we could win at maximum a total of 4,326 clicks through to our website from relevant searches

  • Knowing the average CPC also means we can estimate the cost of getting this traffic through to the site. So, with an average CPC of £0.31 it would cost £1340.94 to get this traffic through.

2.2) INCLUDING CONVERSION DATA

Right, working further down the marketing funnel, now these people are through to your website you need to know how they’re going to behave.


The only way you can do this is to go off of historic conversion data, so you’ll want to know the average e-commerce conversion rate (if you can separate this out by channel for a more accurate figure then even better). You’ll also want to know the average order value.


Add your conversion rate and AOV to your table so it looks like this…

As marketers we can get all the traffic we want to the website but if the conversion rate is terrible, then well, a campaign will be doomed to failure. Conversion rates, like CTR’s will vary hugely depending on a variety of factors so there’s no industry figure. In these instances it’s best to aim low. I tend to include a conversion rate of 1.5% and work up from there to understand the figures.


2.3) THE COST PER CONVERSION

Combining all these figures means we now know more with more accuracy how many people we can send through to the website. Plus based on the historic conversion rates we know more accurately how many of them could convert.


The final few steps are a few basic calculations.


If those new orders generate revenue which is greater than what you spent to get them through to the website then things are starting to look positive. If the result is a negative then you need to review the figures. Either way the results are often surprising.


Although that can often feel like the green light to launch a campaign there’s a few other things you’re going to need to factor in:

  • Will you need someone to manage your campaign? And do the management costs eliminate any profit you stand to make?

  • Based on the manufacturing costs of the product is the markup still significant enough when compared to other marketing channels and their profitability?

  • What is a typical customers lifetime value? The figures we’re working through only cover a single transaction but after their first purchase could they be coming back for more? Therefore, are the figures profitable in the longer run?

  • If targets or budgets have already been agreed by senior management will the campaign reach those goals? Even with a large search demand once you’ve factored in costs and conversion rates the overall number of sales made can be disappointingly few. So, do you need to take a step back and factor in other channels to reach more people? Or invest additional budget into other areas of digital marketing - like CRO - in order to support your Paid campaigns?

3) THE RESULTS


So, let’s run through the numbers to see if the men’s pyjama market is an industry worth where it’s worth launching a campaign:

  • The total men’s pyjama market stands at 150,860 searches each month

  • The average CPC is £1.13

  • With an average 4% CTR it means I could win 4,753 clicks to the website

  • My website converts at a rate of 2.5% which would mean I’d generate 177 orders

  • The average order value is £50.00 so 177 orders at £50.00 gives me a revenue of £8,864.80

  • The cost of getting people to the website is £2,169.70 so what’s left is £6,695.10

Looking through this I’ve noticed a few things:

  • Bidding on competitors branded terms would be expensive and relatively unprofitable, especially compared to the generic campaign searches so if this campaign was to run I wouldn’t include this as a campaign

  • For generic searches a campaign could still generate a revenue of £1,903 even if the conversion rate was a dismal 1.5% so this could work as long as an agency management fee didn’t need to be found

  • If both the conversion rate and CTR were poor (at 1.5% and 2% respectively) then the campaign could only generate an additional £951 in revenue. Based on the unknown levels of competition and the prospect that CPC’s could be higher than estimates then this could leave campaign painfully close to breaking even. Therefore, I’d want to ensure that the Ads are optimised well enough to achieve a higher CPC and my conversion funnel well optimised to ensure this sits above 1.5%

4) BUDGET PLANNING

Ok, so we could leave it there. However, if you’ve got a green light to launch a campaign you need to decide what budget is going to be spent - and more importantly when.

The seasonal demand for most products will vary hugely throughout the year. Even the most non-seasonal of items will typically experience some sort of dip or rise during either the summer holidays or Christmas period. So your budgets need to adjust accordingly to accommodate for this.

To calculate what budget you’re going to need to accommodate for this you need to return to your seasonal average monthly search figures that you exported from Google Keyword Planner:

  • Take the sum of each month so you have 12 months worth of data and from there calculate what percentage of the annual demand this represents

  • Divide your budget accordingly so you have enough to ensure Ads can run as much as possible during months of high demand

With the men’s pyjama market there’s x2 key sales seasons. The Spring, when people switch to lighter, cooler pyjamas and late Autumn/early Winter when people look for warmer pyjamas and Christmas gifts.


4.1) THE CORONAVIRUS CURVE BALL


Google Trends - UK demand for ‘mens pyjamas’ in the last 5 years

Because so much planning is reliant on the last 12 months of data this method’s weaknesses is that it relies on the assumption that the demand of last year will be the same now. With the economic shocks of Coronavirus and the subsequent recession the pattern of demand for certain products can crash or explode depending on the industry with little connection to seasonality.

Therefore, as an added component it’s worth checking the highest volume keywords through Google Trends to assess what the market is doing in a shorter time frame.

You may find that the patterns from previous years are largely similar but then again you may need to adjust your budgets that bit further to include a rise or fall in demand. In this instance the market for men’s pyjamas looks slightly stronger than normal.


4.2) THE RESULTS


Combining these two datasets should give you a crude plan for how to spread your budget.


So for me, I’d need to ensure at least 21% of my budget is kept back for December’s seasonal peak. I’d be making sure that I started planning and testing my campaigns in either January and February for the Spring increase or again throughout the Summer to prePare for the Winter when most sales will happen.

Looking at these changes in demand often reveals:

  • If you’re given sales targets in certain months these can’t be met simply because the demand doesn’t exist

  • When in the year you need to start planning for campaigns launching. If you have a new unoptimised campaign but are competing in a highly seasonal market then it can be better to adjust budgets so you spend more in a quieter month than you originally planned to. That way you have a bigger data set to see the initial performance of your Ads. From there you still have time to make adjustments and optimise the account before the peak of trading

  • If you need to delve deeper into seasonal CPC’s. What’s covered here covers average CPC’s based on 12 months of data. However, campaigns which are fixed around a highly seasonal period of time may need to be looked at again. Therefore, it’s worth exporting the keywords again from Google Keyword Planner just for smaller time period to understand more precisely how the CPC’s may alter. Trading periods like Black Friday can massively inflate CPC’s so you may need to run the figures again to see if your campaigns are still profitable in this environment

WHAT NEXT?


I hope this model helps you on your way to understanding up front how profitable (or unprofitable) running a PPC campaign may be. It may leave you with more questions but at least it points you in the right direction of working out the answers - without spending a fortune in the meantime on unprofitable campaigns.


Remember, these figures represent a starting point. Once a campaign is underway you’ll have more accurate data you can feedback into this model to understand what’s the most effective campaign to drive results through Google Ads.


If you’re also in the mood for some extra planning and are thinking about expanding your offering to international markets then this post on assessing international markets using SEO is a further thorough read. Plus, this post on testing things with Google Ads to make sure what happens in theory happens in practice.


Got questions? Drop me a message here.