Data Analysis Errors A Digital Marketers Make.

Data Analysis Errors a Digital Marketers Make: –

Digital marketers can make mistakes when analyzing data and making decisions.

Reporting and analyzing data inevitably requires a significant amount of time as a digital marketer.

Even experienced marketers can stumble and make some common mistakes when analyzing data and making decisions.

One needs to be on the lookout for mistakes that can result in looking at the wrong data, reaching the wrong conclusions, or leaving the door open to misinterpretations from a customer or boss.

10 common mistakes digital marketers make when analyzing data:

• Without looking at a statistically significant time period.
• Regardless of seasonality.
• Ignore the impact of offline activity.
• Ignore the multichannel commitment.
• Report numbers without conclusions.
• Focusing on the wrong KPIs.
• Decision making based on faulty data.
• It does not incorporate backend data.
• Poor data visualization.
• Assuming everything can be measured.

1. Without considering a statistically significant period of time:

• Many companies see the ebb and flow in lead volume over the course of a week or month, and generally looking at the data for a few days does not provide an accurate reflection of long-term ROI.
• Very few companies will see the exact same number of leads each day or week.
• Marketers should help provide a broader context to minimize fears that numbers will decline on the day that sales increase during the month.

2. Ignore seasonality:

• Another part of considering the weather is taking into account the seasonality factors.
• An eCommerce business will likely see its biggest sales period around Black Friday, while a B2B business may see a drop in lead volume over the holidays.
• Data from previous years can be helpful in taking into account which months tend to have the highest and lowest volume.
• Data directly from Google Analytics and advertising platforms should be taken into account, as well as general sales / leads data from the backend.

3. Ignore the impact of offline activity:

• Many companies see their business come and go based on external factors.
• A SaaS business may see an increase in interest when its biggest competitor raises prices.
• Be on the lookout for news and events that may indicate the potential for increased business interest or reduced inquiries.
• A brand doing traditional advertising must also consider the impact on the brand’s search activity and overall lead metrics when running a TV ad.
• Offline advertising can often lead users to turn to their devices to engage more with a brand.

4. Without taking into account multichannel participation:

• Marketers can become extremely tied to watching a particular channel, be it organic search, paid search, Facebook advertising, or LinkedIn advertising, and obsess over making that channel work.
• No channel operates entirely in a silo, because no web user strictly uses a single channel.
• Analytics and ad platforms that use last-click attribution by default often compound this problem.
• Marketers strictly look at the final source and campaign that generated a lead, without taking into account that a user may have done an unbranded search, clicked on a Facebook ad, and then do a brand search before. . to finally perform the conversion.
• To get away from a purely last-click mindset, pay attention to assisted conversions and conversion paths in the multi-channel funnels section of Google Analytics.
• Use the Attribution section of Google Ads to compare different attribution models.

5. Focus on the wrong KPIs:

• Put together what should consider the perfect digital marketing brief.
• Conversions and conversion rates increased and cost/conversion decreased.
• Campaigns are crushing performance metrics.
• Excited to share the news with the client, to gasp to talk about the report, while the client takes a first look at the results.
• The conversation deviates from the positive conversion stats hoped to focus on.
• Digital marketers have a responsibility to focus on the metrics that most directly relate to the bottom line of the business, but secondary metrics can quickly distract from the end goals of a campaign.
• Both optimization work and reporting to clients or bosses, make sure to focus primarily on the metrics that matter the most (qualified leads from marketing, sales, etc.) over surface metrics like CTR, bounce rate, CPC, etc.

6. Report on figures without conclusions:

• In addition to reporting the correct KPIs, need to communicate why to choose those KPIs and what story they tell.
• If the reports are just charts of numbers and graphs without any context, the client or boss must draw their own conclusions.
• The campaign can accompany comments on what creativity and guidance the brand should try for the next sale based on what worked in this round.
• If performance is poor, talk about factors such as seasonality or offline events that can help explain the decline.
• Providing context can help alleviate concern around a chart line dropping.

7. Decision making based on faulty data:

• Before starting any data analysis, make sure the Google Analytics settings, ad platform conversion tracking, and any other referral tools are set up and measuring the data correctly.
• Ultimately, conversions are not reported if a pixel is not triggered correctly on a thank you page.
• One could over-report the results if a conversion rule is configured for the wrong page.
• Establish a system to periodically verify that data is flowing correctly.
• For example, a developer may have updated a site and left tracking codes in the process, or a customer may have changed the URL of a page without notifying it.
• Ideally, make sure the customer or development team knows to notify before implementing any changes.

8. Do not incorporate backend data:

• Work with multiple B2B clients who have long sales cycles, often involving multiple points of contact before closing a deal.
• While to saw the form submission being tracked in Google Ads, I don’t know how the conversations between the person and the sales team progressed.
• Simply measuring initial conversions doesn’t tell the whole story.
• Proper UTM tagging and source attribution in a CRM will allow measuring how effectively potential customers move through the sales process after they’ve entered.
• Ultimately, with the correct settings in place, one should be able to attribute revenue to a specific campaign, keyword, and ad.
• In the e-commerce realm, review the backend sales data and compare it with what is tracked on the advertising and analytics platforms.
• Identify recurring sales or customers that can be linked to campaigns that have been run, outside of what is directly tracked on ad platforms.

9. Poor data display:

• Charts and graphs can greatly help complex number sets make sense.
• A carelessly used chart can miscommunicate results.

10. Assuming everything can be measured:

• Being able to measure everything can.
• Perfectly correlate with exactly how much dramatic world events have impacted the sales of any particular business.
• Certainly, conclusions can be drawn and correlations found based on data.
• In doing everything can to set up tracking correctly, it should also be noted that no analytics infrastructure is going to represent performance 100% accurately.
• When making data-driven decisions, allow nuances to step back and look at the big picture, including a review of overall marketing results along with those tied to specific channels.

Commit to better data analysis:

• Review the 10 common mistakes, think about own data evaluation and reporting processes.
• Be aware of these potential mistakes when working on decision-making for campaigns, as well as when preparing reports.
• Being able to create better reports and have more effective conversations with stakeholders about where to go after reviewing the results.