Marketing: Go Agile Or Die

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Why Marketing Should Become Agile

Complexity

We are living in the era of technology. Everything new in marketing is created in a digital environment, whilst traditional marketing adds digital elements in order to become more efficient. In general, everything that is digital has a higher complexity level – just compare your messaging apps (e.g. WhatsApp, Viber) with traditional SMS. This universality of technology and its resulting complexity has permeated Marketing teams due to the array of digital tools for monitoring and optimizing both online and offline initiatives: Asana, Photoshop, AdWords, FB Editor, Social Media, Trello, e-mail and the list can continue forever. Ok, there are campaigns that can be implemented with 3 clicks (check universal app installs in AdWords) but a good campaign, for example, requires concept definition, efficient budget allocation, regular monitoring, testing variations & optimization process which make it quite complex task. Without proper planning, complex projects won’t be completed on time with sufficient quality.

Uncertainty

Besides complexity, nowadays marketing industry can be described as a field full of uncertainty. Channels that work yesterday won’t work tomorrow while ad fatigue & wide AdBlock adoption levels make the marketing success equation difficult to be solved. New trends & channels are emerging (from IoT to VR and chatbots) and -due to affordable digital channels pricing (compared to offline & TV)- competition is fierce in many business sectors. Companies that want to stay ahead of curve need to do innovative & bold projects -with the minimum fail percentage- avoiding big launches which may lead to big failures. The ability of a marketing team to make quick re-iterations and setup A/B tests on the fly will define its success in this unstable environment.

Continuous changes

Last but not least, the continuously changing marketing landscape lead marketing teams to face a difficult dilemma: Stay on edge, or stay safe. As Seth Godin insists, “Safe is Risky” so our only option is to stay on edge, but this requires a speedy pace, a testing mentality and a team full of “go-getters” and “doers”. The bigger the company, the more processes it has. Most of them are approval & alignment processes which help us stay safe but don’t move fast enough. In reverse, smaller companies don’t have structured work processes -due to limited resources- which leads to heavy multitasking and no time for project planning & prioritization.

 

dawid-sobolewski-unsplash

 

That’s why Marketing should become Agile. Agile methodology was created years ago in order to help IT to handle complex problems and deliver epic projects on time because IT teams increased their complexity levels many years before marketing did. One of my favorite quotes as marketer is that we do not need to re-invent the wheel to be successful- so it would be better to adopt an existing methodology (that has great legacy and multiple optimization iterations along those years). Agile is excellent for handling complex and fast-changing work environments and allows Marketing to be effective without having to create something new from scratch, having neither the domain knowledge nor the time to do that. There are a lot of interesting Agile frameworks (such as Scrum) that can help you plan your projects better, streamline your workflow & achieve stellar performance.

What I’ve learned in Digital Elite Camp 2017

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For the last couple of months, we (my colleague Dimitris & I) were searching for a conference that will be CRO focused and will help us learn from the best and get actionable ideas of how should we shape our Conversion Rate Optimization strategy onwards. Most of the conferences that we were interested in were located in US (we weren’t be able to do such a big trip for 1 or 2 days conference), or extremely expensive. Until we found Digital Elite Camp a 2-day CRO conference, which located in Tallinn, Estonia, had affordable ticket price and it included presentations from widely known experts such as Morgan Brown and from very popular companies such as Booking.com. Plus, it was co-hosted by Peep Laja who is the founder of the respectable CRO agency ConversionXL.

 

Estonia For The Win

Tallinn is the capital of Estonia which is one of the most advanced countries in terms of digital governance & operations. The city is not so big but it’s very beautiful and Estonians were friendly & helpful. The conference occurred in June, a time period when actually there is no “nighttime”. On June, Estonians celebrate the biggest day of the year which actually means that the sky got dark (not so dark though) about 2 hours/day only!

11Pm in Tallinn, Estonia
11PM in Tallinn, Estonia

 

Digital Elite Camp Key Takeaways

  • Dunning-Kruger effect is cognitive bias where someone is so incompetent that they don’t know that they are incompetent, they think they are tremendous. Beware people, this is very important and cannot be identified by ourselves, always ask for feedback 😉 Moreover, “ego” is not a useful personal trait for CRO, ideas are ideas so let your users decide which one works better for them.
  • Morgan Brown insists that Marketing & Product should be one team in order to have an efficient CRO strategy. Kill silos & create hybrid teams that have no ego, avoid HiPPOs and be obsessed with your users.
  • If you do not know your customers well, then 99% of your assumptions are wrong, just ask your customers! Problem Solution Fit Canvas translate problems into solutions, that will be adopted.
  • When it comes to content the answer is simple: Create something significantly better than your best competitor. Stop creating mediocre content and START creating epic content
  • Best practices are vague. We need to do our own research and adjust best practices to your website & industry. Also, we need more user research. Quantitative data is great when we need to know the “what” but qualitative data help us answer the “why”. When you do user research, Do not listen to your users, observe them and you will understand their pain points.
  • People, Process & Priority are the 3 key pillars for a good CRO program. Check Conversion XL framework, it is very interesting.
  • Identify your north star metric and make everyone obsessed about it. Here is an explanatory article that can help you understand Facebook’s Aha moment, and another one that will help you identify yours.
  • Erin from Booking.com said that we have to be strict and consistent with A/B test documentation. Also, failing, (and learning) is the only way to succeed. Concept != Execution. True story 🙂
  • Your product needs to evolve as fast as your users do, not faster. There is a tendency -due to CRO fast-paced environment- to overcomplicate and change our product faster than our users need to.
  • Understand & learn statistics. In general, CRO experts are not statisticians and statisticians do not care with CRO so there is a gap in here. Work on your evaluation method it really matters. There is an interesting article about Frequentist vs Bayesian method that helped me understand which one works best for my A/B tests.
  • Last but not least, CRO is not just another marketing vertical. Continuous testing is a mentality that needs to be embraced by everyone. Ideas from other departments are more than welcome but you have to prioritize and implement them efficiently.

Bonus Hint: Jeff Sauers shared his learnings about marketing attribution and whether it’s worthwhile for your or not. Here is an interesting GA channel grouping that highlights what many people think when it comes to attribution 😎

interesting GA channel grouping
Interesting GA channel grouping 😎

 

To sum up, Digital Elite Conference was a great conference and I recommend everyone that works in CRO/Marketing to join it next year!

An interesting email concept

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Email marketing

is one of the first digital marketing channels. In general, most of the time when it comes to email marketing, users are seeking relevance & value while marketers are looking for sales & revenue. Companies that align users’ intention with their strategy build loyal customers, while companies that fail, help to produce scientific articles like this (#must read).

On a regular Valentine’s day, a lot of brands send the same emails but they change their subject line. But this year, Sidekick sent me a pure love email, reminding me our relationship and that due to my zero engagement they offered me the chance to “break up”. They also took the blame for it, saying “It’s not you, it’s us”.

So, here is the email copy:

This email:

  • Has great concept
    It’s a brave and perfectly timed concept, in which brand may lose subscribers but they will resurrect users as well -like me-.
  • Highly relevant image
    A heart and a question in pink background fit perfect.
  • Easily readable copy
    Email copy consists of targeted bold & italic letters that helped me get the message fast and easy.
  • Interesting CTA
    I’ve seen a lot of CTAs like this and it seems like it’s a trend so we expect to see more in the future. Interesting approach, let’s see if it works.

Email marketing is here to stay and we need to continually explore new concepts in order to stay relevant and provide value to our users.

 

 

Marketing Attribution Models: Go Beyond Last Click

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Attribution models help assign credit for sales and conversions to touchpoints in conversion paths and they are part of fundamental optimization techniques for marketing channels. Although it’s not hot topic (like conversion attribution window), it is a mandatory process, especially for companies that have large advertising budgets and marketers who want to allocate their money in a sophisticated and efficient way.

Default Last Click Attribution Model

Default last-click attribution would be a good solution if users made just one interaction before they convert. Just check (into your Google Analytics) your users’ type allocation (returning vs new visitors) and realize that for each returning visitor there are at least 2 touch points, which means that if we use last-click attribution we’ll lose minimum 50% of the conversion value overall. Yes, at least half of the conversion value (best case scenario), gone. Now, imagine that you have 10 interactions before a conversion and you will realize that you lost 90% (!) of the conversion value overall.

I am not going to re-invent the wheel about attribution models, Avinash made our day easier about them with this comprehensive article, providing all the necessary information about default attribution models (along with their pros & cons) and his preferred custom attribution model as well. I just want to share a different point of view based on personal learnings about marketing attribution models.

Lessons Learned: Attribution Models

Ode to Time Decay

If you don’t want to create your own custom attribution model, I would suggest selecting Time Decay attribution model (click-only). Adding time dimension within an attribution model make it more “human” because it doesn’t rely only on clicks (which on mobile can be accidental clicks) and impressions (which most probably are not viewable). This model is useful for digital touchpoints that drive conversions AND touchpoints that increase the likelihood of a conversion in the near future. Please double check and confirm your lookback window before you implement this model. The time decay factor (click-only) calculated as follows:

TDF is defined by two things: (1) the time span the interaction is away from the conversion x and (2) the time decay parameter p. The smaller p is, the quicker the value of the TDF decreases with increasing distance of the interaction event from the conversion.

 

Custom attribution model

There are a lot of custom models out there (from “bathtub model” to this “mind-blowing” model) but most of the times we have to create our own attribution model in order to meet our needs and solve our problems. If we want to create our own attribution model we need to consider the following:

Time lag 

We need to check the time lag of our conversions in order to add it in our attribution model. A big-time lag means that the user needs more time to convert, so the initial touchpoints don’t influence him equally with touchpoints that are closer to conversion (avoid time periods with seasonality, for more relevant results). For more information about time lag data check here.

Path Length

Path length is critical. Along with time lag will help us define our approach. If the main time lag and page length are big (ex. 90 days – 4+ touchpoints), then we should consider giving more credit to touchpoints that are close to final conversion. If the main path length and time lag are small (ex. 3 days – 2 touchpoints) then we can maybe split equally the conversion value because all touchpoints matter almost the same.

Interaction types/Engagement adjustments

“Click” interaction type is the most useful since Google Analytics does not track impressions and rich media interactions from other channels besides Google products. You can add impressions to your attribution model and specify the relationship between general impression weighting and relative time weighting. Finally, add user engagement metrics and distribute credit proportionally based on page depth rather than time on site.

 

Different models solve different problems

As Jeff Sauer said in Digital Elite Camp 2017: “When it comes to attribution models, there is no silver bullet”. The question “which channels are better for awareness” can be answered by first-click model while the question “which channels are better for closing deals” can be answered by last-click. If you are savvy enough, you can answer more complicated answers (for example you can check specific AdWords campaigns and non-direct user paths performance), using custom models that will give you a clear view of your users’ behavior.

TIP: Conversion segments can be useful too especially when you want to explore interactions (any, first, assisted, last), demographics (city, device, language) and conversion value (comparing paths of high value and small value conversions)

 

Google Analytics Premium: use the data-driven attribution model

If you use Google Analytics Premium you have 2 additional tools that you can use, ROI Analysis & Model Explorer (both in Beta). The model explorer displays the weighted average value for path positions prior to conversion for each channel based on data-driven attribution model. I believe that this model works by identifying patterns among those leads that lead to conversions.

% reflects the overall weighting of a channel at a particular position in the path.

From the report above we can see that while paid search and display help users initially connect with the website, referral, organic search & direct are close to the end of the conversion path.

 

Attributing value to offline marketing channels

All models above are very good for digital marketing but when it comes to offline channels they are useless. A good approach that can help us attribute our budget efficiently across offline marketing is exit poll and NPS. Exit poll is a window that is displayed after a placed order (depending on the industry, it is displayed on every order or on the 1st order of every user) and simply asks the user which channel helped him learn about our company. Exit poll is a good tool to allocate orders in channels but when it comes to high-value and low-value customers it is lacking depth and sophistication so we can implement NPS technique as well. NPS (which means Net Promoter Score) ask users how likely is to recommend our product to other users and can be asked immediately after exit poll. One good strategy for companies that have both offline & online marketing channels is to include all channels in Exit poll and then correlate GA data along with exit poll and NPS in order to create a solid attribution framework.

TIP: For exit poll and NPS you don’t need any complicated tools, you can use survey tools that collect feedback and then match feedback with user or order id.

 

All models have pros and cons. Digital models lack cross-device tracking, while offline models don’t have 100% answer rate. Attribution modeling is a complex project for marketing professionals but none underestimate its value. Try to track all your channels (online and offline),  check a couple of models and their results and come up with something that will help you understand where performance and high-value customers are coming from even though you don’t have all the data in place. As Charles Babbage said: “Errors using inadequate data are much less than those using no data at all”

 

Building Actionable Data Driven Buyer Personas

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After gathering valuable data-driven information from our platforms, it’s time to build actionable buyer personas that can be applied to performance marketing campaigns and help us shape an effective & relevant communication/targeting strategy. We use a template that consists of various elements, from demographical to psychographic data that help us understand our buyer personas, “who” they are and “why they are buying. Gathering psychographic data can be done via surveys and client interviews, but you can find a lot of hints in Google Analytics & FB audience insights as well. Now let’s stop talk the talk and just walk the walk.

Let’s assume that we own Google merchandise store (that we have data for) and we are allowed to create our own strategy (besides Google’s universal strategy).

Actionable Data-Driven Buyer Persona Template

  • Buyer Persona Name (and/or nickname)
    The name or nickname gives you a quick overview of this specific buyer persona.  It doesn’t have to have a real name, but it should be something common that will help everyone understand the basic info of this persona.
  • Image/Quote
    A relevant image and a representative quote would help us visually shape our buyer persona, avoiding long written description copy. Try to find an image with context and make a research in order to find a relevant quote for your persona.
  • Demographics
    Initial data for each persona should include information such as age, gender, location, marital & employment status that can be used for messaging optimization & campaign targeting. Since we have e-commerce website we should consider buyer personas tech savvy level.
  • Interests/Activities
    This section includes interests & affinities of our buyer persona. A normal buyer persona should have multiple interests that may overlap between personas. About activities, we should include not only work related topics but lifestyle topics as well (after all, we are talking to humans, not robots). What your users care about, how do their spend their day, what do they like etc. Those interests/activities can be used in Facebook & AdWords campaigns as targeting options.
  • Preferred Websites/Apps
    From advertising placements perspective, preferred websites are the best guide. If your most valuable users are using YouTube heavily then you should consider to do video marketing and promote it on YouTube or Vimeo. Combining “preferred websites” with “interests” is a good way to create highly relevant concepts to specific advertising placements, do native advertising or a nice activation initiative with a specific website/app. This section can help us adjust our copy and content as well.
  • Goals/Needs
    Here we include opinions goals & needs that are related with our buyer personas. What do they think, what are their goals, what do they want to achieve them, what are their needs/pains etc. After all, humans are emotional beings so the reasoning behind most of our decisions is emotion related. Five Factor Model is a good model that can help us understand the goals and needs of our personas. This section is valuable in terms of communication, copy & content rather than performance.
  • When/Where/How/Why Persona uses our product
    By answering the questions above we can get actionable insights that can be used in our marketing campaigns. “When” can help us do efficient time adjustments in campaigns, “where” & “how” answer can help us incorporate priming effect in our ads while “why” can help us craft unique selling proposition copy that can increase our ad relevancy.

Based on data from previous post, our main buyer persona is Californian young male technophile professionals. I will create my persona and provide all the actions that I’ll do based on existing information.

 

Californian Young Male Technophile Professional
  • Buyer Persona Name (and/or nickname)
    John Smith the Geek
  • Image/Quote
    Image: Image of a “nerdy” young professional with glasses
    Quote: “AI is here to stay”

    Demographics What can we do
    Age:
    25-34
    Apply age targeting in FB/AdWords campaigns
    (not strict one though, but “play around target ages) 
    Gender:
    Male
    Apply gender targeting in FB/AdWords campaigns,
    consider “unknown” gender as well
    Marital status:
    Married
    Employment status:
    Works as engineer
    Apply interests targeting in FB/AdWords campaigns,
    include specific companies in FB targeting.
    Tech savvy:
    Yes
    Location:
    US, California
    Apply location targeting in FB/AdWords campaigns,
    (add miles withing targeted area in FB)

     

     

    Interests/Activities What can we do
    Participates in conferences   Participate/Sponsor those conferences
    Rarely clicks on ads Invest in SEO
    Likes traveling Do surveys and give travel prizes
    Likes Sports Participate/Sponsor local sport events
    Reads blogs & forums Build blog, provide relevant content
    Likes rock music Hire a rockstar (I’m joking)



    Preferred Websites/Apps What can we do
    YouTube, Facebook Do display marketing in these specific channels
    Growthackers, Inbound, Quora, Medium  Answer questions, actively participate in forum discussions, re-post your blog posts
    SUMO Provide a compelling offer in SUMO



    Goals/Needs What can we do
    Openness to experience Provide fresh, out of the box solutions
    Has lack of focus Need to invest in remarketing in order to stay top of his mind
    Want to be up to date with his fellows/industry Provide relative testimonials, include localized/industry social proof numbers  
    Seeks self-actualization Avoid buzzwords, Use images of people that seem successful and confident
    Don’t actively looks for offers, but likes VFM deals   Use anchoring effect & provide compelling offers



    When/Where/How/Why Persona use our product  What can we do
    Use our products in daily routine Use photos of regular people that having fun, working out, relaxing at home
    Use our products at home Use photos of married couples, being happy at their home
    Use our products because they love our brand Make our logo visible and create products that are aligned with our brand’s strategy & style


This buyer personas framework help us build an efficient communication & performance strategy, especially when we target users that had no prior connection with us (new cookies) and had little or no awareness of our brand. So before you start spending budget, invest time & effort learning about your main buyer personas and talk to them like you know them.

 

The First Step for Data-Driven Buyer Personas

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Since the dawn of marketing, buyer personas were the targeting & messaging core for every brand. Now that the era of mass advertising is dying, and digital marketing providing us with powerful capabilities (from user targeting standpoint), identifying the right buyer personas is more important than ever. I want to share our methodology (step-by-step) of how to create actionable data-driven buyer personas that drive our actions and decisions in marketing. This first blog post is dedicated to the essential step of data gathering that provides us with valuable information about our users. The more relevant information we have about our users, the better user personas we will create.

For data gathering process, we use information that is based on Google Analytics & Facebook audience insights data.

Google Analytics*

Google Analytics is the “homepage” for many marketers when they search for quantitative data but it can be used as a rich source of qualitative data as well. Enabling demographics and interests reports will allow Analytics to collect additional information from the DoubleClick cookie (web activity) and from Device Advertising IDs (app activity). Demographics, Interests and Geographical data will give us most of the qualitative information that we need. 

*All data are from Google Demo Account

Age / Gender
Google Analytics - Age / Gender
Please note that this report is based on 58-65% of sessions, because Google can provide us data only for users that opted in.

Age & gender information is the first piece of buyer personas data gathering. In order to have that data, Google Analytics gets data from DoubleClick -Google’s programmatic advertising platform- and creates automated groups for the key metric that we’ll set (I suggest keeping sessions as the key metric). From data above, we understand that the group of users that are between 18 – 44 years old is driving more than 80% of traffic overall. Moreover, it is clear that male audience prefers our products more than female audience.

TIP: Due to best key metric in Overview tab being”sessions”, it is recommended to dig deeper into Age & Gender data in order to focus on transactional/performance data rather than sessions.

Here is a good example. Although most of users that come to our website are between 18-24 years old, they have relatively low conversion rate. Actually, their performance is directly compared with an age group that isn’t within our primary focus (45-54 years old). This can help us adjust our selected age groups and identify potential challenges & hidden opportunities.

Transactions of our “main” age group 18-24 are fewer than age group 45-54 that isn’t within our focus

 

Advanced Segment Creation

Advanced Segments

Now that we know the age groups and the gender of users that we want to focus, we can implement advanced segments and separate our selected audience from all users. Summary tab will indicate us if segments that we create are big enough and are worthwhile analyze them. We can build as many advanced segments as we want but we can compare up to 4 at one time.

 

Device

Google Analytics - Device

Device overview helps us understand specific devices in selected segments that drive traffic and performance.  Desktop is clearly the best device, although mobile has good potential as well due to decent amount of traffic.

 

Analyzing traffic & performance metrics is a good way to understand where visits come from and if they convert or not.

 

Geographical Data

Google Analytics - Geographical data

Geographical data indicate where our users located. United States are dominant both for performance & sessions. Although it is just one country, additional analysis can help us dig deeper and narrow our focus in specific cities.

“TIP: Check cities tab as well. This can help you laser target your audience both in offline (billboards) & digital (Facebook/Google) marketing. Also, add any secondary dimension where applicable. In our example, I’ll check cities along with devices so I can understand how people in specific cities are reaching my website.”

 

City/Device

Google Analytics - Buyer personas

Users that are located in Mountain View and use desktop can help us shape an efficient & actionable data-driven buyer personas that consist of users that have more than double conversion rate from website traffic overall. There we go! 🙂

 

Language

Google Analytics - Language

No questions asked English (US) is the only language that we will use for our messaging.

 

Now, I want to check users’ interests. In interests tab, there are 3 sub-categories: Overview, Affinity Categories, In-Market Segments and Other Categories. All of them provide data for your users. Google’s interest categorization is kind of messy but in general, it works like this: Affinity Category (broad interest) /Other Category (specific sub-category of the broad interest) / In-Market Segment (users buying behavior).

Interests Overview 

Google Analytics - Interests overview

I prefer to explore 1-2 affinity categories for each buyer persona (since they are too broad) and 4-5 Other Categories & In-Market segments that are more specific. From data above, seems that our users are technophiles (they love technology), like watch TV/movies (kind of vague interest) and have specific preferences in Mobile Phones (that are highly correlated with technology), employment and travel. Finally, they like online videos, arts & entertainment and they are active in social media.

 

TIP: Replicate your top buyer personas and do some “reverse” research. In this example Males, Technophiles 25-44 are the most valuable users. So, I can use advanced segments in order to create buyer personas and compare it against all users. Let’s do this for channel performance evaluation.

Channel performance

Google Analytics - Channel Performance

From the analysis above I understand that our most valuable users are:

  • Between 18-44 years old
  • Males
  • Prefer desktop
  • Live in US (Mountain View specifically)
  • Speak English
  • Love technology

 

Facebook Audience Insights

Audience Insights is a tool that is provided by Facebook and gives us qualitative data about Facebook users. This data comes from two main sources:

  1. Self-reported Facebook data: This information is given by users when they fill in their personal information, including age, gender, relationship status etc.
  2. Third-party data partners: Household income, home value & purchasing behavior information is available through external companies that match these data with Facebook IDs. Please note that this information is available only for U.S audiences.

There are 3 ways to categorize users in Facebook:

  • Generic category that consists of data from generic user demographic & interest groups
  • Connection category that includes data from people that are connected to our Facebook fan page, place or app.
  • Custom audiences that consist of data from users that engage with our website (from visitors to most frequent users)

I mostly use custom audiences that include visitors & buyers from my website, but for our example, I will use demographic data from users that are located in California and are interested in Google services (as we seen in GA data before).

 

Age & Gender

Facebook Audience Insights - Age/Gender

This report gives us information about gender & age for our audience -along with general FB benchmarks-. Note that although the percentages are not the same, the dominant audience sector is almost the same with GA data.

 

Job Title

Facebook Audience Insights - Job Title

Job title provides data for industries where people work, based on self-reported data on Facebook. IT, Technical, Computer & Mathematics have correlation with Google Analytics “Technophiles” audience, so the correlation between GA & FB data is still on.

 

Relationship Status – Educational Level

Relationship status-Education level

Basic relationship & education level data inform us that users are mostly married and college graduates while singles & grad school have potential as well.

 

Top Categories

Top Categories inform us about top pages for every category that can help us understand our personas and optimize our targeting & messaging. For example, if you wonder which TV channel should you use for your TV commercial here you can find some good insights. Moreover, this information can help us define ideal placements for our online (display) or offline (TV, Partnerships etc.) marketing.

 

Page Likes

Page likes indicate Facebook pages that are likely to be relevant to your audience based on Facebook page likes. Relevance provides us pages that are most likely to be relevant to our audience based on affinity, page size and the number of people in our audience who already like that page. Affinity score gives us benchmark score about how likely our audience is to like a given page compared to everyone on Facebook.

 

Frequency of Activities

This report gives us behavioral data based on frequency of selected activities. Based on this we see here our targeted audience doesn’t consist heavy social media users.

 

Device Users

Desktop (computer) has the biggest percentage of usage, while the combination of desktop & mobile has the biggest share of traffic. Targeting desktop & mobile devices is mandatory and we should consider a tracking solution for cross-device conversion (but that is another topic).

 

Below there is data that is available on for US audience and give us more depth and details about our targeted audience, but I won’t dig deeper in this post.

Household Income/House Ownership

 

Household Size/Household Value

 

Spending Methods

 

Retail Spending/Online Purchases

 

Purchase Behavior

 

In Market for a Vehicle

 

Lifestyle

Facebook Audience Insights - Lifestyle

 

Now, combining Google Analytics & Facebook Insights data, we know that our primary user is:

  • between 25-34 years old,
  • male
  • married (at least the biggest percentage),
  • prefers desktop & mobile devices,
  • lives in US, California,
  • speaks English,
  • works & likes technology
  • likes specific pages
  • has a lot of specific aspects that can be applied in specific concepts.

 

As you can see Google analytics & Facebook audience insights can provide us with a big amount of data. What we have to do now, is to use them wisely in order to build actionable data-driven buyer personas that can help us effectively connect with our users and amplify our performance.

Conversion Attribution Window: Set The Best

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If I’d say that conversion attribution window is a hot topic I’ll lie. BUT the way that you’ll set (or just keep default option) your conversion attribution windows for campaigns & GA will define your performance overall. Most of the marketers are not aware of that and keep windows as they are which means that they use the default 30 days click-based window for Google Analytics and AdWords & 28 days click-based (plus 1-day view-based) for Facebook. This is not necessarily bad, but most of the times you can adjust it to your company’s strategy and have a clear view that will help you evaluate your efforts efficiently.

If you don’t remember or you don’t know what is conversion attribution window check here.

Really, what is the best attribution window for your campaigns?

The answer to this question lies within another question which is:

How many days does a user need to convert?

Usually, B2B companies need more days to acquire a customer (a typical flow is: lead – demo or free trial – customer), while B2C companies need fewer days for user conversion ( users need just to register & perform their purchase or sometimes don’t need to register at all). Common attribution windows (besides default ones) are 14 days click for B2C & 60 days click for B2B.

One good method to define the ideal attribution window is to consider Google Analytics Time LagTime Lag data is my preferred way to set the best conversion attribution window regardless business sector (B2B/B2C). Note: You have to define goals and/or collect e-commerce transaction data in order for Multi-channel funnel reports to have data.

Time Lag Data

You can find “Time Lag” tab into your Google Analytics account in “Conversions” -> “Multi-channel Funnels” tab. This will help you understand how many days does a user need in order to convert.

Here are the settings of Time Lag tab:

Conversion

Not all your goals are equally valuable. Select different goals and focus on those that matter the most, in order to find your ideal time lag.

time lag - conversion type

 

Type

Choose if you want to see all the data or only data for AdWords.

 

Interaction Type

Here you can stick to clicks because Google Analytics cannot track Impression & Rich Media from other platforms besides Google Network.

Time Lag - Interaction Type

 

Lookback Window

Lookback Window would be a good way to experiment with different windows. Find the best time lag by checking conversions percentage in “distribution” tab.

Time Lag - Lookback WIndow

 

Conversion Segments

If you want to be more specific about time lags for different channels you need to compare conversion segments. Here you can see the default segments and compare up to four segments.

Time Lag - Conversion Segment

 

Custom Conversion Path

Custom conversion paths are very useful but you need to have advanced knowledge of attribution modeling and know what you are looking for so you can set the right path.

You can create path options that include interactions (any/first/last/assisting), a couple of dimensions -mostly ad dimensions- & a number of occurrences.

Time Lag - Custom Conversion Segment

 

How many days does your user need to convert?

 

Things to consider:

Keep in mind that you can set your attribution window anywhere from 1 to 90 days in GA.

Moreover, In case you have more than one goals you need to be sure that your selected window applies in every goal that matters to you.

Last but not least, remember that Facebook and AdWords/GA have different conversion attribution windows and they are not connected (so there is no holistic attribution modeling applied). Facebook Atlas and other tracking software (ex. Adjust) have conversion attribution windows as well. Feel free to check them all, but I recommend to experiment with attribution window in GA at first and evaluate performance from this specific source. 

Conversion attribution windows along with attribution modeling are very interesting topics but not for everyone. Companies that spend large advertising budgets need to be sure that they optimize their performance based on users behavior and not just default settings of advertising & tracking platforms.