3 Essential Plays to Transform Yourself from a Tactical to Strategic Marketer

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:

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:

  1. Your company is running ads across multiple channels and campaigns.
  2. You have over a million records of online + offline data that must be analyzed holistically.
  3. The analytics you have access to are static and/or siloed in multiple platforms.
  4. You’re planning your marketing mix and budgeting for a new quarter.
  5. There’s friction with sales over lead quality and marketing’s contribution to revenue.


  1. Analyze data from multiple sources together
  2. Defend spend and performance across channels and campaigns
  3. Better allocate budget and resources
  4. Forecast and deliver high-quality, high-converting leads to sales
  5. Optimize campaign messaging
  6. Quickly course correct to remain in budget and generate positive ROI

Play Prep:


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.


Marketing Operations

Campaign Managers

Growth Marketers

Digital Marketers

Social Media Marketers

Sales Operations

Customer Success

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.


First Touch

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.

Last Touch

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.



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.

Time decay

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.


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:


Most ad platforms and marketing tools allow the user to export data in spreadsheet formats that can be merged together for analysis.

Key Considerations

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.


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.

Key Considerations

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.


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.

Key Considerations

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.

    Marketing Play #2

    Target Your Highest Value Prospects for Maximum ROI

    Target segment analysis is the process of dividing customers into groups, or cohorts, with similar attributes, behaviors, and experiences to determine which are the most profitable. Marketing campaigns and messaging can then be tailored to these specific segments, improving conversion rates, acquisition costs, and revenue.

    When to consider this play:

    1. You need to identify, refine, or expand your ideal customer profile(s).
    2. Your company runs dozens, hundreds, or even thousands of campaigns across channels and needs to optimize targeting, budget, and messaging.
    3. You need to forecast and deliver high-quality, high-converting leads to sales.
    4. You’re looking to help maximize customer retention and lifetime value.


    1. Determine who your top customers are
    2. Identify common traits and behaviors for your most valuable prospects
    3. Forecast and deliver high-quality, high-converting leads to sales
    4. Identify niche or untapped markets
    5. Maximize campaign conversion rates while minimizing acquisition costs
    6. Better understand customers to improve loyalty and lifetime value

    Play Prep:


    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.

    Social media data – Twitter, LinkedIn, etc.

    Customer data – Zendesk, Intercom, etc.


    Marketing Operations

    Campaign Managers

    Growth Marketers

    Digital Marketers

    Social Media Marketers

    Sales Operations

    Customer Success

    Executing the Play:

    1. Get a single view of the customer

    Today’s customer journey is a labyrinth of dozens of touchpoints with multiple onramps. To identify and understand your top targets, you need to capture as much information about them as possible as they move through the funnel.

    Individual marketing platforms represent narrow, siloed slices of the customer journey. Sales and customer data from tools like Salesforce and Zendesk must also be integrated to get the full picture of the end-to-end customer journey. This leaves marketers with 3 options to create a single, unified view across sources:


    1. 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. However, this method is a tedious, time-consuming, and error-prone manual process that often causes spreadsheets to crash due to scale limitations.


    2. Software integrations and APIs

    Most marketing 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 for easier analysis. This method is mostly automated but may still require some manual work for data sources that are not supported by the platform. Additionally, integrated data is often only available in aggregate and can’t be drilled into.


    3. Cloud data exploration and business intelligence (BI)

    Unlike traditional analytics tools, cloud-native BI solutions empower 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. Because data comes directly from the BI team’s data cloud, it’s always complete, accurate, and up to date. Users can combine dozens of data sources and analyze billions of rows of data in just a few clicks without asking for technical help or running into scalability issues.

    2. Build customer data profiles

    Once you have a single customer view, you can build customer profiles that include the traits and behaviors your business needs to effectively understand, reach, and convert different audience segments. It’s likely you won’t have all of the data you’d like, but companies like Clearbit and ZoomInfo can help enrich datasets to better understand the people engaging with your marketing initiatives.

    Depending on your business model, customers, and campaigns, some types of information are more helpful for segmentation than others:


    Demographic Demographic segmentation is one of the most basic and common types of market segmentation and includes grouping audiences by things like age, sex, marital status, family size, occupation, education level, income, race, nationality and religion.

    Psychographic Psychographic segmentations groups audiences by personal motivations, beliefs, lifestyles, personality traits, values, opinions, and interests. This can be helpful if your target market is very large and broad.


    Geographic Geographic segmentation is a subdivision of demographic segmentation and groups audiences by city, state, country, zip code, or other regional classifications.

    Behavior Behavioral segmentation groups audiences by common behaviors including product usage, purchase behaviors, content consumption, engagement, and more.


    Firmographic Firmographic segmentation is a type of demographic segmentation that focuses on the attributes of organizations instead of individuals. Companies can be grouped together by industry, employee count, revenue, location, and more.

    3. Identify core metrics with dollar-based outcomes

    Marketers have many metrics they use to indicate the success of particular approaches or programs: ad clicks, social media likes, email open rates, etc. However, target segment analysis must be grounded in metrics that go beyond the marketing funnel and tie performance directly to dollar-based outcomes.

    It’s critical for marketing, sales, and customer success to align on these metrics and what constitutes a high-value cohort. These metrics will vary depending on your business type and goals (e.g. B2B vs B2C, or securing net new business versus upsells), but some common examples include:

    • Customer acquisition cost (CAC)
    • Customer lifetime value (LTV)
    • Specific products purchased
    • Average contract value (ACV)
    • Net revenue retention (NRR)
    • Time from first touch to purchase 

    4. Get comfortable with cohort analysis

    Cohort: A group of people that share common characteristics, behaviors, and/or experiences.

    Cohort Analysis: An analysis technique that divides data sets with information about users, prospects, or customers into smaller subsets based on shared characteristics, behaviors, and/or experiences within a given timeframe or funnel.

    We recommend marketers looking to identify their highest value targets start their cohort analysis by grouping users according to the metrics they identified in the previous step — e.g. by acquisition cost, lifetime value, etc.

    Cohort analysis is extremely powerful, but the accuracy and insightfulness of the results depend largely on the tool teams use to conduct the analysis. For maximum efficiencies and best results, use a modern, cloud-native analytics and business intelligence solution that approaches cohort analysis with the following 3 capabilities in mind:

    1. Speed: Insights are only as good as they are recent. Cohort analyses should be done querying live data, not data extracts, to provide the most recent and valuable updates. This becomes especially critical when you have a narrow or fixed window of opportunity to operate in (i.e. you’re running a promotion that lasts just a few days, you just introduced a new product, it’s a holiday sale, or a store just opened).
    2. Scale: The power of the cloud enables marketers to quickly crunch high volumes of data across dozens of sources. When more questions can be asked of the data and more people are empowered to discover the answers for themselves, the quality and speed of insights grows exponentially. Most marketing platforms present data in aggregate and don’t allow users to dig into data at a granular level. But the ability to slice cohorts down to SKU level for instance often yields the most surprising and impactful insights
    3. Exploration: Every cohort analysis should start with an educated hypothesis, but identifying your highest value cohorts requires trial and error. Rapid iteration and data exploration is necessary to create, discover, and refine cohorts based on an unlimited combination of data points. This requires choosing a solution with a user interface that is easy to learn and use, regardless of technical expertise.

    To see how fast and easy cohort analysis is in Sigma watch this short two-minute video.

    Once you have identified your best and worst-performing cohorts based on these metrics, you can begin further analyzing and grouping them by the profile data points you collected in Step 2. Do customers within these groups work for companies in similar industries? Do they come from the same lead sources? Do they share particular demographics such as age or education level?

    It’s important to look at both good and poor-performing segments to reconcile any similarities between the two. For example, customers with the lowest acquisition cost may come from Twitter. However, customers with the lowest lifetime value may also come from Twitter. Therefore, it doesn’t make sense to focus all of your ad dollars on Twitter.

    5. Optimize your marketing campaigns

    Armed with a detailed, accurate, and up-to-date picture of your most valuable customers all the way through their journeys, you can begin target prospects with similar profiles using highly relevant messages. You can also see where opportunities lie to drive upsells, cross-sells, and additional purchases.

    Sample Scenario : Your team decides to allocate more marketing budget toward digital advertising. But after a few months, you notice customer retention rates are dropping. After segmenting your customers by conversion channel, you see that retention rates are higher for users that come in organically through the blog vs. those that come in through paid ads. Drilling down even further, you discover that users that enter through the blog and then download gated content also convert 20% faster. Your team can then decide to reallocate more of your budget toward organic content creation.

    6. Target segment analysis success story

    Migo, a cloud-based platform that enables B2C companies in emerging markets to offer credit to their customers, was in full growth mode at the start of 202     0. But the challenges brought on by the COVID-19 pandemic required the company to quickly pivot. “We had to very quickly shift gears and focus on customer retention and loan recovery,” explains Alex Harvey, Migo’s Marketing Lead.

    Using Sigma, Alex was able to segment Migo’s population of defaulted borrowers and identify the most effective channels to reach them, and craft the right messages for each of those channels.The team was then able to build out and launch a series of robust recovery campaigns to re-engage these customers.

    Because we had immediate access to all of our data, could run any analysis without the assistance of the BI team, and knew the data was accurate, we were able to pivot and relaunch our entire marketing strategy in just 30 days.” he continues. The result? A 47% increase in campaign response rates.

    Alex Harvey

    Marketing Manager at Migo

    Marketing Play #3

    Build a customer journey 360
    to deliver personalized and seamless experiences

    A customer journey 360 is a complete view of your company’s entire relationship cycle with a single customer, and includes every interaction they have with your brand. Analyzing each customer touchpoint and subsequent outcomes in order enables marketers to optimize journeys and create more personalized experiences that increase conversions, satisfaction, and up/cross-sells.

    When to consider this play:

    1. You need to better understand the customer journey and ideal path to purchase.
    2. You want to accelerate conversions by providing personalized customer experiences.
    3. You need to more accurately forecast and deliver high-converting leads to sales.
    4. Customers are experiencing a disjointed journey as they move across touchpoints owned by different internal departments.
    5. There’s an opportunity to build upsell or cross-sell campaigns.


    1. Identify if your intended customer journey is optimal or even in the right order.
    2. Identify gaps and opportunities to drive conversion.
    3. Create more cohesive customer journeys across departments.
    4. Personalize all customer interactions.
    5. Refine customer behavior models and forecast accuracy.
    6. Increase campaign conversion rates while minimizing acquisition costs.
    7. Maximize customer retention and lifetime value.

    Play Prep:


    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.


    Marketing Operations

    Campaign Managers

    Growth Marketers

    Digital Marketers

    Sales Operations

    Customer Success

    Executing the Play:

    1. Map your customer journey

    Before you can measure and optimize the effectiveness of your customer journey, you must have a clear picture of your current customer touchpoints and their intended purpose. The easiest way to do this is to map each potential interaction to a specific stage within the marketing and sales funnel.




    Twitter, Google search, display ads


    Ebooks, webinars, blog posts


    Case studies, free trial, demo video


    Pricing page, sales proposal template


    Email nurture, office hours

    Note that it’s okay to start broad at this stage for certain touchpoints, like “blog posts.” You can drill deeper into specific blog posts as you get further into your analysis.

    2. Capture and integrate data across sources in a single place

    Next, identify where data across each of these potential customer touchpoints lives.

    These are your data sources. During this process you may realize you are not currently capturing data around particular touchpoints. Now is the time to remedy that!

    Common data sources include:

    • CRM software
    • POS systems
    • Marketing automation platforms
    • Website analytics tools
    • Customer service solutions
    • Social media
    • Email or ad platforms
    • Your own product logs
    • Trade show lead lists
    • Customer review sites

    Individually, each of these data sources provides only a slice of the customer journey, but when integrated together they provide the complete customer 360. To achieve this, start by asking your data or BI team if you have a data platform and whether the data sources you have identified are being pulled in.


    Talk to your BI team about implementing one. Compared with traditional solutions, the Snowflake Data Cloud leverages the unlimited scale and speed of the cloud, making it easy to store, manage, and analyze massive volumes of live data from across sources. The Snowflake Data Cloud also enables sharing live access to governed datasets, reducing the cost and complexity of managing pipelines while increasing the freshness of your data.


    Simply ask your BI team to start integrating your desired data sources into your Snowflake account. Since Snowflake is elastic, it can handle all types of data, and separate data storage and compute costs, so this shouldn’t be an issue. Pre-built data connectors like Fivetran can also help.


    Check with your BI team to see if you have an analytics and business intelligence (BI) solution designed for the data cloud. Most BI tools weren’t built with marketers in mind. They require specialized coding skills to perform even the simplest queries, leaving marketers stuck waiting on BI teams to help, or turning to outdated, error-prone spreadsheets.

    But modern, cloud-native BI tools like Sigma eliminate these technical barriers, allowing marketers to use a spreadsheet interface to analyze and explore live data directly from the Data Cloud — without writing a single line of code. Combine dozens of data sources and crunch billions of rows of data at the lowest level of detail in just a few clicks!

    3. Break customers into key cohorts

    Once you’ve mapped out the customer journey and its corresponding data sources, it’s time to understand what customers are engaging with which touchpoints. Start by breaking customers into cohorts, or groups of people that share common characteristics, behaviors, and/or experiences within a given timeframe or funnel.

    It’s critical that cohort analysis is based on live, real-time data at the most granular level of detail. In addition, cohort analysis is an iterative process that requires significant data exploration. That’s why the easiest, fastest, and most accurate way to conduct your cohort analysis is by using the same cloud-native BI tool you leveraged in Step 2.

    We recommend segmenting your customers into at least some of the following cohorts for your customer 360:

    • Fastest time from first touch to purchase
    • Slowest time from first touch to purchase
    • Highest lifetime value
    • Longest customer relationships
    • Number of purchases
    • Value of purchases
    • Churned customers

    4. Reevaluate your customer journey

    Examine each cohort’s journey through your funnel, paying special attention to points of entrance, acceleration, and churn. Ask yourself the following questions:

    • What touchpoints did they engage with?
    • What touchpoints did they skip altogether?
    • In what order did they interact with these touchpoints?
    • Did they hit any touchpoints you may have forgotten?

    Look for key behavioral similarities and differences between your cohorts. As mentioned in Step 1, now is the time to get more granular around any broad touchpoints you included in your journey map. E.g., if you listed “webinars” as a touchpoint, it helps to understand which cohorts attend which specific webinars.

    Once you have reconciled any conflicting insights between high-performing and low-performing cohorts, edit your customer journey map. Remove any touchpoints that customers skipped or only low-performing customers engaged with, and refine any initially broad touchpoints to be more specific and prescriptive.

    This new map represents your ideal customer journey and the path your marketing, sales, and customer success efforts should lead potential and current customers down.

    Sample Scenario #1: Your company makes a music production app for mobile devices. You design an attractive landing page filled with CTAs directing people toward your app’s listing on various mobile app stores. But after analyzing the customer journey across segments, you find that most people discover your app through the app stores themselves, bypassing your landing page entirely. With this insight in mind, your team focuses on app store optimization – improving screen shots, keywords and copy on your app’s listings. You also begin a targeted review campaign to get your most engaged users to leave positive reviews in the app stores.

    5. Identify opportunities to personalize

    Personalized calls to action convert 202% better than generic ones, according to HubSpot. Creating personalized experiences wherever possible in your ideal customer journey will significantly increase marketing’s effectiveness.


    Consumers are 2x more likelyto view personalized offers as important.

    72% of consumers say they only engage with personalized messaging.

    Nearly 85% of consumers will share their data to have a personalized experience.

    Once customers and prospects have “identified” themselves through an email address submission or publicly-available corporate IP, you can begin capturing additional information about their demographics, firmographics, and behaviors to personalize various touchpoints. Here are some popular examples:

    • Adding a prospect’s first name to a button in a website or email
    • Automatically directing website visitors to particular pages based on job title
    • Recommending specific products based on a customers’ previous purchase
    • Including a prospect’s company name in digital advertisements
    • Sending special offers immediately following interactions with customer support

    Pro tip: While prospects and customers may not “identify” themselves until further in the funnel, tools like FullContact allow marketers to backfill information about these consumers, which can then be used to personalize future touchpoints! Companies like Clearbit and ZoomInfo can also help enrich datasets to better understand the people engaging with your marketing initiatives.

    6. Customer 360 success story

    Yesware, a popular sales productivity platform, used Sigma to get full visibility into the customer funnel so they could optimize their ad spend and messaging.

    “Before it was difficult to separate which users were coming from a pure acquisition basis and which were recommended from existing accounts,” says Ian Adams, Yesware’s VP of Sales and Marketing. “It was unclear how many new trials we were getting from our ad spend.”

    One surprising insight the team at Yesware uncovered through building their 360 customer view was that 50% of trials of their popular Google Chrome browser extension start with no interaction with the marketing site.

    We discovered that a significant number of people were starting a trial on a secondary Gmail account, in addition to starting from the Chrome Web Store, and bypassed the marketing site entirely. This was a great insight we would have never been able to uncover without Sigma.

    Luke Bussey

    Growth Consultant at Yesware

    Using this insight, the team was able to take a more surgical approach to optimizing their funnel. The result? Customer acquisition costs have been slashed in half and data-driven marketing decisions are made 50% faster.

    Strategic Marketers Leverage the Full Power of Data

    Top performing marketers are able to strategically use data to generate consistent and repeatable results. They must think beyond a single campaign or channel, and start taking a holistic approach that aligns their efforts with business outcomes.

    This requires effectively narrowing their aim, reaching the most profitable audiences, and driving the best long-term opportunities for their company. It also depends on achieving a complete 360º view into the entire funnel and beyond to develop the seamless experiences customers are demanding today.

    Modern BI tools like Sigma and cloud data platforms like Snowflake enable marketers to quickly combine and analyze a wide range of data sources, independently uncover answers to critical questions to optimize campaigns on-demand, and adapt to rapidly changing market conditions in real time. The results? Lower acquisition costs, higher conversion rates, increased customer loyalty, and happier sales teams.

    Are you ready to move beyond tactics and become a strategic marketer?

    Visit www.sigmacomputing.com and www.snowflake.com today!

    About Sigma

    Sigma is the only cloud analytics and business intelligence platform empowering business teams to break free from the confines of the dashboard, explore data for themselves, and make better, faster decisions. The award-winning platform was built to capitalize on the performance power of cloud data warehouses to combine data sources and analyze billions of rows of data instantly via an intuitive, spreadsheet-like interface – no coding required. Sigma automates workflows and balances data access with unparalleled data governance to make self-service data exploration safe for the first time.

    To learn more and start a free 14-day trial, visit www.sigmacomputing.com

    About Snowflake

    Snowflake delivers the Data Cloud — a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Join Snowflake customers, partners, and data providers already taking their businesses to new frontiers in the Data Cloud. snowflake.com.