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Data Driven Decisions : NCC

Updated: Nov 18, 2023

Introduction


You want to leverage the full potential of business performance indicators and technology?


The answer is simple and complex too, you need a full understanding of how one specific aspect impacts the other and in turn, hypothesize on future endeavours. Then repeat this process over and over again, weekly, monthly, quarterly and yearly.


In order to do this, we need to reverse engineer our strengths and weaknesses. This is more complicated when you are just starting out, but especially efficient for established activities.


How to visualize ...


We like to use the Pirate Framework: AAARRR


This allows us to break down all the aspects of growth into one clear full-funnel. Like pieces of a puzzle that make up for the full image.


Awareness, Acquisition, Activation, Revenue, Recurrent, Referral


In each step, we can have statistics from different sources: Ads, website, financials, referrals, etc...



Ex: An ad gets 1000 views for 10 euros (A1) 100 people visited the website (A2), 10 people bought items (A3), for an average order of 100 euros (R1), they later come back 6 months later to make the same purchase (R2) and recommend the website to 3 friends (R3)


This is a grossly oversimplified version, but in the essence... Makes sense for most activities.


Let's imagine that only 1 person visited the website, then we know it is a concern in step A1 and that the ads are not good enough. We can repeat this line of thought for the next steps by analysing the data.


However, say that one of our ads is doing really well... We can use it as a reference to create similar ads. Hence, the positive/negative thinking when analysing business metrics.


From there, we can create more hypotheses to soften the negatives and enhance/scale the positives. We recommend having the analysis done by professionals, and then you can have the actual changes carried out by 3rd parties or in-house. We can go a whole step further by finding negatives in the positives, and positives in the negatives. If that makes any sense to you...


Marketing Vortex


In order to illustrate this previous example more "in depth", we need to visualize our funnel. And let's face it, a funnel sounds a bit too simple, doesn't it?


Let's picture vortices instead. They have infinite outer layers revolving around an axis. Each layer put together creates the circular shape.




The main idea is to have the extreme outside layer pictured as the most general data we have (total impressions from ads, search engines) then go down step-by-step. If you really takes this exercise to its core, you will not only be doing marketing, but business as it is.


Google Search Console (General awareness A1 data)


For the purpose of simplicity in this example, we will take round numbers and skip some important data. Replicate this using concrete data & thorough analysis/calculus.

A1

100 000 views from ads

300 000 views on Google

5% Click-through-rate

​General data

A2

55 000 visitors

55% Bounce rate

2:45 Avg session duration

Customer experience data

A3

1300 add-to-carts

2.2% of add to carts on all users

300 new blog subscribers

​Activation data

R1

​550 orders

40% payment completion rate

100 euros average order value

Revenue & margins data

R2

10% Returning visitors rate

6 months avg time between purchases

​500 euros average lifetime value

​Loyalty & returning data

R3

100 Number of referrals

​5% Referral conversion rate

10% Referral attainability

Referral data


Now you might be asking yourself, why is it a vortex?


All the layers are indefinitely expandable, and connected. Each layer is composed of several sub layers. The "centre" is hard to change (R1), obviously the most significant part of the vortex, as it is constituted of your core business model, but the outside layers are entirely up to how well you built your marketing experience and how you use the revenues.


Parts of your vortex need to be attenuated, like a bad payment processor which would result in a R1 decrease, or a bad customer experience which would result in a A3 decrease, bad ads and google indexing would result in a A1 decrease etc... These are typical things we look to fix, having reversed engineered the data. Meaning that every layer can also affect another layer.


Sample data without in-depth stats


On the other hand, the beginning and end of the funnel is where the magic happens. It is what we call "growth hacking", if you can grasp awareness, experience, sales, loyalty and how your brand spreads through referrals, you can scale an activity.


You can then visualize this data using graphs, charts and comparison to time frames or competitors.





There are infinite layers because you can always create new ways of attracting new customers and on the other side investing the return on investments into new endeavours. So let's say it's a vortex but also a worm-hole.







More examples:


The "Welcome" offer (A3)

Generate more activations by giving out a promotion for first time shoppers. This is also called reciprocity, which when giving something to someone, they feel like they have to give back something in return.


"Thank you" note (R2)

Adding a simple & customized "thank you" post-purchase email with a promotion for the next time they are shopping can generate recurrent purchases. Also, can give a referral opportunity in the same email for R3



Benchmarking


All of this is nice, but how do I know what is a positive or negative ?


You need to be able to access statistics from similar companies and compare, we have a general idea of what looks healthy or not, but only after having worked with so many clients. We observe each industry can have its own challenges. In instance, simple marketing costs are expected to be higher in dense markets with lots of competition.


A/B Optimization


The basics are: 1. You need enough sample data and 2. You need to be able to compare changes from one group of users to another or comparing previous time frames.


Say we changed how fast the website loads, this would be the type of changes we could observe on a A2 level on a week-to-week comparison.


Google really enjoyed this type of optimization and started seeing more positive changes


We then tackled retargeting by action types

  • Consult blog post → Ad (Acquisition)

  • Unfinished purchase → Ad (Activation)

  • Clicked ad → Ad in x time (Activation)

  • Bought +3 months ago → Ad (Recurrent)

On funnel level, this means that we are able to create more structured actions by creating specific ads based on behaviour performed on our channels.


We could keep giving examples similar to what's above, for all the different aspects involved, but then we'd basically tell you everything, and magicians only tell you about their simplest tricks.


Dynamic Audiences + Creatives


In order to illustrate long-term campaigns, we wanted to show you what it might look like on structured data layers...

Using existing technologies, we can augment the data by connecting different sources.








We can then aim to structure campaigns ex:

1. Awareness (Video for example)

2. Acquisition (Image ads to 50% video viewers)

3. Activation (Retarget 7-day visitors)

4. Etc... R1 R2 R3

- Exclude buyers / high repetition


One of the biggest challenges here becomes the creative sourcing, meaning how many are we able to test on a weekly or monthly basis, which will avoid exhaustion. Since our audiences are more long-run, creatives have to change so that we don't see a decrease in results.


Which brings me to my next point, dynamic creatives are, for example, a mix of new releases and bestsellers, but that update automatically. It is another great way to make campaigns perennial through time. There are some really cool AI tools being developed for this specific issue, where you can input your best ads and create thousands of similar actions. Once all of this is done, we can aim to scale through different bidding techniques, budget optimization, and more... Budget fixed vs ROAS




SEO / Content Strategies


To us the biggest challenges in generating organic traffic is definitely content planning & creation, large corporations pay authors incredible amounts of money for good content, but what makes it good ? Yes, the content itself is import but what about context ?


Here are some things to look out for:


Is it 'digest' (meaning the opposite of huge paragraphs like here)

Is it interactive (are there accordions, animations, etc)

Is it properly linked to relevant content (internal / external) and to commercial goals


Will it actually drive traffic ? Competitor analysis, trends, keywords volumes, etc

Technical analysis: Usually done by developers with SEO knowledge


Relavant data to specific pages: Bounce rates, time spent on page, flow, etc...


Intuitive Infrastructure


If you observe companies who spend hundreds of thousands of dollars per year on their website, you will find one common theme: simplicity & logic. So much so, that some websites look a bit bland. White background with a modern text, simple navigation and clear advantages.


This style is so recurrent because it is efficient for the customer. Just like you, they don't like wasting time on useless visuals and crazy colours. Even more important is to intuitively answer questions the customer might be asking himself.


Some questions might be simple to answer, like what is the shipping time?

But why would I trust this brand? Becomes a challenge.


There are a couple of aspects we like to think about when intuitively making purchases...

- Design & presentation

- Social proofing (Reviews, community Instagram posts, etc)

- Authority (About, partner logos)

- Reciprocity

- Consistency

- Urgency & Scarcity


Let's make some questions to get you started...


Awareness: What are all the sources that make us visible to potential customers & who are they?

This data will be from a lot of sources (PR, Google organic & ads, Facebook & Instagram, LinkedIn, Twitter, etc...)

Acquisition: How are we presenting our products or services ? What are the key questions our audience needs answers for?

Activation: How are they interacting with said presentations ?

Revenue: How much money are we making from optimizing the steps above and below ?

Recurrent: What is the shopping behaviour of my existing customers ?

Referral: How likely are we to be recommended to others ?


Are you ready to make impactful changes?


If you want a full case study based on this principle, with financial & marketing data, do not hesitate to contact us at hello@nocodecenter.com







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