3 Essential Plays to Transform Yourself from a Tactical to Strategic Marketer
Marketers have more sophisticated tools and greater access to data than ever before. But along with these expanding capabilities comes increasing challenges and expectations. Today’s C-suite and sales teams demand marketing leaders move beyond simple tactic-level execution to strategic thinking that aligns with larger business goals and ultimately drives revenue.
This requires savvy marketers to use data to identify their top performing prospects, connect with them in cost-effective ways, and tailor brand messaging to personalize the experience across the entire customer journey.
But becoming a modern marketer is easier said than done. The customer journey has expanded to dozens of touch points across multiple, siloed platforms that generate billions of data points. It’s hard enough to accurately measure results across the disparate campaigns, let alone weave together these efforts into a cohesive and unified experience that drives conversions.
That’s why the data and analytics experts at Snowflake and Sigma have put their heads together to compile three essential marketing plays loaded with actionable insights and step-by-step guidance to help you transform from tactical to strategic marketing:
MARKETING PLAY #2
These no-fluff, easy-to-implement guides are packed to the brim with actionable strategies so you can become a strategic marketer that consistently generates exponential results and gets the recognition you deserve.
Marketing Play #1
Nail Your Marketing Spend Attribution for Better ROI & Improved Decision-Making
Marketing attribution is the science of determining which touchpoints, channels, and messages along the customer journey have the greatest impact on conversion. It empowers modern marketers to better allocate spend, improve decision making, and refine their strategy to maximize ROI.
When to consider this play:
- Your company is running ads across multiple channels and campaigns.
- You have over a million records of online + offline data that must be analyzed holistically.
- The analytics you have access to are static and/or siloed in multiple platforms.
- You’re planning your marketing mix and budgeting for a new quarter.
- There’s friction with sales over lead quality and marketing’s contribution to revenue.
- Analyze data from multiple sources together
- Defend spend and performance across channels and campaigns
- Better allocate budget and resources
- Forecast and deliver high-quality, high-converting leads to sales
- Optimize campaign messaging
- Quickly course correct to remain in budget and generate positive ROI
CRM data – Salesforce, Zoho, Pipedrive, etc.
MAP data – Marketo, Hubspot, Pardot, Eloqua, etc.
Website analytics – Google Analytics, Kissmetrics, etc.
Ad platform data – Facebook, Google Ads, Twitter, etc.
Campaign data – Uberflip, Terminus, Adobe Campaign, etc.
Customer data – Zendesk, Intercom, etc.
Social Media Marketers
Executing the Play:
1. Select your attribution model
Attribution models use different analytical techniques to assign the appropriate level of impact, conversion value, or amount of credit to each marketing touchpoint. The effectiveness of each model depends on a variety of factors. Every organization has their own unique customer journey, buying cycle, and business model.
SINGLE TOUCH ATTRIBUTION MODELS
Gives all credit to the initial customer touchpoint and ignores any subsequent interactions. Great for brands with highly transactional business models, but provides little insight when leads must be nurtured over time.
Gives all credit to the last interaction a customer has before converting. Ignores all other touch points along the customer journey that may have had a significant impact on the purchase decision.
MULTI-TOUCH ATTRIBUTION MODELS
All touch points across the customer journey are given equal credit. Forces teams to consider the end-to-end customer journey, but makes it difficult to identify high vs. low-performing interactions and optimize campaigns accordingly.
Gives increasing credit to touchpoints customers engage with closer to conversion. Assumes that the touchpoints closest to conversion had greater impact on the sale, but can minimize the initial touchpoint that acquired the lead.
Position based (U-shaped)
Gives 40% credit to both first and last touches and evenly distributes the remaining 20% across all others. Recognizes the different kinds of touches used to acquire and convert a lead, but may underestimate middle-of-funnel (MOFU) touchpoints.
CUSTOM DATA SCIENCE MODELS
The most challenging and time intensive model consists of applying machine learning models to your breadth of customer, channel, product and sales data. While costly, this method may also give you the most accurate representation of your buyer’s journey and the impact of each engagement on the business.
2. Integrate your data
Out-of-the-box dashboards and reports from individual marketing platforms reveal only part of the customer journey. This data needs to be joined with data from sales, customer success, and all other marketing channels to provide a full picture. There are several options for integrating this data, each with its own considerations:
DOWNLOADING CSVS AND MERGING THEM IN A SPREADSHEET
Most ad platforms and marketing tools allow the user to export data in spreadsheet formats that can be merged together for analysis.
Each marketing platform formats data differently, so significant normalization must take place before it can be effectively merged together.
Granular, row-level data may not be accessible.
Downloaded data is not real-time, so insights quickly go stale and must be updated on a regular basis.
Manual merging and frequent updates increase the likelihood of errors and inconsistencies.
Extracts cause data silos and varying versions of the “truth,” while sharing these reports creates version control issues, as well as security and governance risks.
Large volumes of data with a million+ rows created by marketing campaigns cause spreadsheets to become extremely slow and crash.
SOFTWARE INTEGRATIONS AND APIS
Most marketing analytics and automation platforms have integrations or APIs that hook directly into other popular tools like Salesforce or Zendesk. Data is automatically passed between these solutions and integrated together for easier analysis.
Eliminate much of the manual work associated with joining data together, saving time and reducing errors along the way.
Many marketing channels are not supported via integrations and must still be manually joined via a spreadsheet.
There are usually limitations around what data can be pulled in and how it can be manipulated.
Integrated data is often available in aggregate only and cannot be drilled into.
CLOUD DATA EXPLORATION AND BUSINESS INTELLIGENCE (BI)
Unlike traditional analytics tools, cloud-native analytics and BI solutions empower line of business teams like marketing to easily access, integrate, and analyze data at the same level as data and BI experts — no coding or technical expertise required.
Data is accessed directly from the data cloud — your company’s single source of truth for data — so it’s always complete, fresh, and accurate.
No more dealing with fragile, costly, and complex data pipelines. Data can be shared seamlessly across systems without copies, moving data, or complex integrations.
Access 3rd party data through ‘shares’ rather than copies, increasing the quality and timeliness of your analysis.
Since you are querying data that already exists in your data cloud, your team can be certain that they are operating off of the most accurate and fresh data set.
Eliminate the need for data or coding expertise by adopting the form and function of tools business teams are already familiar with, like spreadsheets.
Combining data from dozens of sources into one holistic view can be done in hours instead of weeks.
It’s possible to analyze billions of rows of data at once without hitting scale limitations.
Data is available and accessible at the lowest level of detail for granular insights.
Analyses are done once, saved, and automatically updated, making it safe to share or reuse them and eliminating version control issues or data silos.
Give marketers the power to create rich, interactive dashboards and/or drill down into the underlying data to ask follow up questions.
3. Analyze and optimize
Once your attribution model has been chosen and your data well integrated, you can get a clear and accurate picture of marketing performance across each channel.
It’s critical to go beyond acquisition-level metrics like CPC (cost per click) and CPM (cost per thousand impressions) into the conversion metrics that will reveal which campaigns are actually contributing to revenue, such as:
- Channel spend vs channel revenue
- Cost per lead
- # of leads generated
- # of marketing qualified leads (MQLs)
- # of sales qualified leads (SQLs)
- Opportunity Velocity by Source
- Average Opportunity Value by Source/Campaign
- Cost per MQL
- Cost per SQL
- Amount of pipeline generated
- Customer lifetime value (LTV)
While some all-in-one marketing platforms present some of this data in aggregate, an advantage of using a cloud-native analytics and BI solution is the ability to easily click into the individual campaign data. This granular visibility reveals patterns, spikes, and trends that make it possible to not only identify the top performing channels overall, but also the specific touchpoints and messages having the greatest impact.
Sample Scenario : LinkedIn advertising is beginning to underperform. Before we assume that the channel has low ROI and is not worth our investment, it pays to investigate. Using a cloud data exploration tool, we drill into the individual, weekly campaign data and notice our top five ads are actually performing much better than average with ICP titles and are generating more late-stage pipeline. The solution is to reallocate LinkedIn funds to those top-performing ads and cut spending on the rest. We can also identify why the messaging of those particular ads proved to resonate and apply these insights to other channels.
4. Marketing attribution success story
Yesware, a popular sales productivity platform, used Sigma’s spreadsheet UI to join four years of marketing pageview and product trial data in just a few days — without ever typing a single line of code.
First, the team matched every single site pageview and tracking event with anonymous user IDs. Once a user was identified through a trial signup, they backfilled the data and mapped these anonymous users to known email addresses.
Next, Yesware leveraged the Snowflake Data Cloud to combine data from Customer.io, Google Analytics, Salesforce, Zendesk, and Google and Facebook ads for a complete view of user behavior at each stage of the funnel.
Sigma gave them the power to analyze, optimize, and attribute ROI at every touchpoint, resulting in a 50% reduction in customer acquisition cost.
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