Mobile search drives billions of calls to business each year, and calls convert at a higher rate than digital leads. When properly optimized, calls can have a transformational impact on your bottom line. Join this webinar to learn tactical tips and smart strategies to boost your PPC results with call analytics.
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The era of graphs and spreadsheets as a way of thinking about analytics is beginning to approach its end. Predictive analytics, along with associated artificial intelligence (AI) and machine learning technologies, are changing the way in which we deal with data. These tools are becoming more accessible, and ‘big data’ thinking is no longer limited to firms with billion dollar budgets.
Predictive analytics provides a glimpse into the future, as well as access to strategic insights that can open up new opportunities. Here are five ways you can put predictive analytics to use, and how you can change the way you think about data.
According to Forrester research, predictive analytics has found three main use cases for dealing with leads. Specifically:
- Predictive scoring: This method analyzes how leads are responding to your marketing attempts and how likely they are to take action based on that information. In this way, you can more quickly identify which leads to focus more resources on and which to divert resources from.
- Identification models: This use case is an approach that focuses on comparing leads to customers who have taken actions in the past. In doing so, you can divert resources to those leads who are most promising based on previous actions they have taken, as well as identify new markets that you weren’t previously aware of.
- Personalization: In concert with predicting which leads are most likely to take which actions, the same data can be used to determine which leads respond best to which types of messaging. This advanced form of segmentation can take things deeper than simply splitting leads into groups – instead sending them much more personalized messages.
One prominent example of this was covered in the Harvard Business Review, detailing how a Harley Davidson dealership increased sales leads by 2930% using an AI named Albert.
The AI crunched CRM data to identify characteristics and behaviors of previous buyers. It then split them into micro-segments based on those characteristics. For each segment, it tested different combinations of headlines, visuals, and other elements to determine which worked best for each segment.
The value of your lead qualification is highly dependent on the value and quantity of your data. No matter how good your statistical models are, their abilities are still very limited without access to the information that they need to learn about your customers.
In the digital space – particularly if you are not using a CRM – the best place to start with predictive analytics will almost certainly be an integration of Google Analytics and Google BigQuery.
Modeling customer behavior
While lead qualification and conversion is the most obvious use-case for predictive analytics, and likely the one worth looking into first, it’s far from the only marketing application of this emerging technology. But virtually any use is going to have customer modeling at its core.
You can divide customer modeling into three basic types: cluster models, propensity models, and collaborative filtering.
Clustering is a way of segmenting customers into groups based on many variables. A cluster model looks for correlations between various attributes and identifies a number of equilibria in which certain types of attributes tend to accumulate. What makes clustering special, compared with traditional segmentation, is the sheer number of variables involved. Clusters often use 30 variables or more, far more than would be possible if you were manually segmenting customers, or even if they were manually segmenting themselves.
Clusters come in three forms:
- Product clusters: These are clusters of customers who tend to only buy specific types of products, ignoring other things in your catalog
- Brand clusters: These customers tend to buy from a specific collection of brands
- Behavioral clusters: These are segments of customers with a specific collection of behaviors, such as frequent buyers who place small orders, or customers who prefer the call center over the checkout cart.
What’s important to recognize about these clusters is that they enable predictions about which clusters people belong to – even with limited information. If they buy one product with a specific brand, your brand cluster can predict what other brands they may be interested in, rather than just the more obvious recommendation of simply offering everything else by the same brand.
A propensity model is one that makes future predictions about customer behavior based on correlations with other behaviors and attributes. This may be accomplished using regression analysis or machine learning. A good propensity model controls for as many variables as possible so that correlations aren’t confused for causes.
Here are a few examples of propensity models:
- Propensity to unsubscribe: A model like this allows you to determine the appropriate email frequency, weighing the possibility that a recipient will unsubscribe against any possible positive outcome
- Propensity to churn: These are customers who are likely to move on if you don’t take action, but who may be high value otherwise
- Lifetime value: Modeling the lifetime value of a customer can help you make strategic marketing decisions if it leads you to customers with more lifetime value, or leads to behaviors that extend lifetime value.
Other propensity models include predicting how far through somebody’s lifetime value you are, and how likely they are to convert or buy.
If you’ve seen Amazon’s “customers who liked this product, also liked…” recommendations, you know what type of model this is. At first glance collaborative filtering might sound similar to product-based cluster models, but collaborative filtering is a bit different. Rather than grouping customers by the types of products they are likely to buy, collaborative filters make recommendations based on aggregate behavior.
In other words, this is less about the user’s product preferences and more about the behaviors that products tend to cause for users.
There are three types of collaborative filters:
- Up-sell recommendations. These are recommendations for a higher tier version of a product before the sale is made
- Cross-sell recommendations. Also offered before the sale is made, this is a recommendation for a product that is often bought at the same time as the initial one
- Follow-up recommendations. These are recommendations for products that people tend to buy a certain time period after buying a prior product, such as replacing a product that runs out, or buying dishes after buying a table.
Connecting the right product to the right market
Working backwards from customer modeling, it’s possible to identify markets for your products that you may not have been aware of. Here are just a few examples of how this use case can play out:
- Incorporate referral sources into your cluster models. This will allow you to identify which traffic sources correlate with which types of products, brands, or behaviors. From this, you can immediately identify a new market for these products or brands
- Incorporate referral sources into your lifetime value propensity models. This will allow you to determine which locations to invest more of your marketing resources into, since you roughly know what the ROI will be
- Look for correlations between traffic sources and success with up-sells, cross-sells, and follow-up recommendations
- Look for correlations between keywords and your customer models
- Analyze the attributes that are strong predictors of buying specific types of products and brainstorm other markets that might share those attributes that you have not yet targeted
- Investigate high performing outliers where limited data is available and investigate whether expanding in those markets is a good option.
Connecting the right users to the right content
There are a number of ways that you can leverage your customer models to connect prospects with content in ways that move you toward your goals, some of them more obvious than others. Here are a few examples:
- Matching content related to products or brands based on the appropriate clusters
- Matching users to conversion copy when propensity models predict they are most likely to buy
- Recommending content to users that improves their propensity scores
- Recommending content to users that enhances their likelihood of responding well to an up-sell or cross-sell
- Matching traffic sources to the content that tends to produce high propensity scores for each particular traffic source.
As you can see, the number of approaches you can take here grows pretty quickly. Think strategically about how best to put your models to use and make the most of your models.
Discovering strategic marketing insights
While some predictive analytics tools can automatically streamline your marketing process and generate results (like Albert did for Harley Davidson), it’s important to remember that human decisions still play a very important part in the process.
Where predictive analytics and related AI tools often fail is in a propensity to ‘over-fit’ the data. They can get stuck at local maximums and minimums, incapable of making the leap to new terrain.
Escaping from traps like these, and making the most of these tools in general, requires you to find strategic insights from within your predictive analytics models.
For example, suppose you discover that a specific piece of content has a tendency to raise your prospects’ propensity scores; any automation you have in place can be applied to customize how your users are marketed to, and push them toward that piece of content. But what predictive analytics can’t tell you is whether there might be other traffic sources you haven’t tried yet that would be a good fit for that content. Using your experience and brainstorming capabilities, you can identify other potential markets for that content, feed them into your model, and see how the exposure changes things.
Your goal in working with these kinds of models must always be to find insights like these and test them to see if the results are as expected. If your model runs on autopilot it will not discover any new opportunities alone.
Google is always evolving.
Tweaks to the algorithm and changes to how search results are presented are often lamented by frustrated marketers, but these evolutions can also present valuable new opportunities for making content visible to searchers.
Today’s SERPs are a far cry from the humble list of ten results and handful of sponsored links that Google started out with.
In an effort to help searchers find their information faster – and with less clicking and scrolling – Google is incorporating a wider range of rich features into its results pages.
I want to use this post to take a look at some of these features.
How can we ensure our content is best suited to Google’s ever-more intuitive results pages? And how can we make it stand out there?
1. Google’s Answer Features
Answer boxes are increasingly being used by Google in an effort to provide searchers with answers to their questions, even without the need for clicking through to a site.
This can be annoying for marketers who have traditionally been working to optimize their content to garner clicks from Google. The more the SERPs provide answers to an increasing number of questions, the harder it will become to persuade searchers to click through.
This is all the more reason to start understanding how Google seeks to answer.
Searching around the term ‘doulas’ highlights how intuitive Google is at knowing what questions to answer (even if just a single word is searched for). Also, we can quickly see the diversity of answer boxes displayed from very subtle differences between search phrases.
Looking at the phrase ‘doulas,’ even though we haven’t asked Google a question, much of the above-the-fold SERPs are dedicated to an answer box. In this case, giving us related questions.
With the search phrase ‘whats a doula’ Google is even more confident, expecting us to most likely be satisfied with the dictionary definition of ‘doula’.
And with the question ‘what do doulas do’ Google gives us two answer boxes above the fold – the first is an info box, and the second is another related questions box.
These are just a few examples. Info boxes can vary in format from ranking lists, to numbered steps, and tables. Searches that relate to events are more frequently resulting in answer boxes that show the dates of the event.
Additionally, subjects such as weather and stock provide their own nicely displayed answer boxes too.
It’s good to think about this when it comes to creating content for your own site.
‘Doulas’ may be a relatively competitive search on its own, but it’s easy to see how a piece of content that answers a wider question about the niche (e.g. ‘how much does it cost to have a doula?’ or ‘why a doula is important?’) can be presented high in the SERPs even if the domain is not ranking in the top organic spots for the original search.
As Google declares, it is making the decision on what to show here programmatically. That is to say, unlike other rich features within contemporary SERPs, webmasters can not simply use mark-up to signal to Google that a certain piece of content contains an answer.
This means marketers and writers need to prioritise ensuring that the content answers their (well-researched) keyphrase promptly, naturally and succinctly on the page.
Video content – particularly from YouTube – remains a good way to stand out in Google’s increasingly content rich SERPs.
This search for ‘homemade baby carrier’ (you can see a theme developing here) ranks How To Make Your Own Baby Carrier… well but also quickly alerts the searcher to a number of important attributes about the content:
- It’s a ‘how-to’
- It’s a short-ish video
- The name of the brand is focused on the niche
- There’s a clear, action-led description (‘you need…’)
In short, there’s a lot of enticing content around that video result which might result in a click. It is authoritative and concise.
Even though the domain keepcalmandcarrythem.com is not visible on page 1 of Google’s rankings for this term, their brand is very visible thanks to this video.
3. Local, hyperlocal and business optimization
I covered some of this in my hyperlocal SEO piece recently on Search Engine Watch.
Google is increasingly good at presenting local, hyperlocal and business information. Businesses can upload their information to Google My Business, focus on local/hyperlocal/business key phrases, and mark up their content to make it even easier for the search engine to display relevant business information via rich features such as:
Knowledge graphs and panels
Note: Conscious Birthing have a good opportunity to improve this knowledge panel from Google for ‘doulas’ by simply updating their Google My Business profile.
All these attributes go further to helping your content stand out in the SERPs when people search for local, hyperlocal or business key phrases.
Depending on the search a user is making, Google is also very savvy when it comes to delivering image results.
We sometimes see them delivered next to rich snippets, accompanying information in answer boxes, and often in image packs.
Referring back to our earlier ‘homemade baby carrier’ search, the lead spot in the SERPs is given over to a feature pack of eight images.
The first image is a still from a YouTube video which doesn’t rank in the natural results. The second image is from a site called thediymommy.com – again, this domain doesn’t feature anywhere else in the front page results.
But they prove the point: Good images, if optimized well, can be very visible in Google – even if the content is outranked in text terms.
In this example, the info box content is taken from abckidsinc.com while the image is taken from wikihow.com. Both feature in the natural results in this instance, but both are below the fold and lower than two informational YouTube videos.
5. Don’t overlook SEO best practice and PPC
Just because Google continues to incorporate more intuitive and rich features in its SERPs, it does not mean we should overlook vintage SEO such as good title tags and meta descriptions. Well written content in regards to these can still make you stand out.
Also, we have not yet mentioned PPC, Google Shopping and sponsored listings. Of course, if your site is of the ecommerce type, it is still possible to use such tools to gain and maintain visibility in Google.
The search engine giant is still as keen to create revenue from ad clicks as it is to make its user experience quicker and more efficient.
That said, the changing landscape of Google’s SERPs is a challenge but is also ripe with opportunities.
If your content is well-planned, well-made, and well-optimized – Google will reward you with visibility.
Where should you focus when you have some unexpected downtime? Explore a few opportunities that have helped improve my long-term effectiveness as a PPC Account Manager.
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What are query stream ontologies, and how might they change search?
Search engines trained us to use keywords when we searched – to try to guess what words or phrases might be the best ones to use to try to find something we are interested in. That we might have a situational or informational need to find out more about. Keywords were an important and essential part of SEO – trying to get pages to rank highly in search results for certain keywords found in queries that people would search for. SEOs still optimize pages for keywords, hoping to use a combination of information retrieval relevance scores and link-based PageRank scores, to get pages to rank highly in search results.
With Google moving towards a knowledge-based attempt to find “things” rather than “strings”, we are seeing patents that focus upon returning results that provide answers to questions in search results. One of those from January describes how query stream ontologies might be created from searcher’s queries, that can be used to respond to fact-based questions using information about attributes of entities.
There is a white paper from Google co-authored by the same people who are the inventors of this patent published around the time this patent was filed in 2014, and it is worth spending time reading through. The paper is titled, Biperpedia: An Ontology for Search Applications
The patent (and paper) both focus upon the importance of structured data. The summary for the patent tells us this:
Search engines often are designed to recognize queries that can be answered by structured data. As such, they may invest heavily in creating and maintaining high-precision databases. While conventional databases in this context typically have a relatively wide coverage of entities, the number of attributes they model (e.g., GDP, CAPITAL, ANTHEM) is relatively small.
The patent is:
Identifying entity attributes
Inventors: Alon Yitzchak Halevy, Fei Wu, Steven Euijong Whang and Rahul Gupta
Assignee: Google Inc. (Mountain View, CA)
US Patent: 9,864,795
Granted: January 9, 2018
Filed: October 28, 2014
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ontology of entity attributes. One of the methods includes extracting a plurality of attributes based upon a plurality of queries; and constructing an ontology based upon the plurality of attributes and a plurality of entity classes.
The paper echoes sentiments in the patent, with statements such as this one:
For the first time in the history of the Web, structured data is a first-class citizen among search results. The main search engines make significant efforts to recognize when a user’s query can be answered using structured data.
To cut right to the heart of what this patent covers, it’s worth pulling out the first claim from the patent that expresses how much of an impact this patent may have from a knowledge-based approach to collecting data and indexing information on the Web. Like most patent language, it’s a long passage that tends to run on, but it is very detailed about the process that this patent covers:
1. A method comprising: generating an ontology of class-attribute pairs, wherein each class that occurs in the class-attribute pairs of the ontology is a class of entities and each attribute occurring in the class-attribute pairs of the ontology is an attribute of the respective entities in the class of the class-attribute pair in which the attribute occurs, wherein each attribute in the class-attribute pairs has one or more domains of instances to which the attribute applies and a range that is either a class of entities or a type of data, and wherein generating the ontology comprises: obtaining class-entity data representing a set of classes and, for each class, entities belonging to the class as instances of the class; obtaining a plurality of entity-attribute pairs, wherein each entity-attribute pair identifies an entity that is represented in the class-entity data and a candidate attribute for the entity; determining a plurality of attribute extraction patterns from occurrences of the entities identified by the entity-attribute pairs with the candidate attributes identified by the entity-attribute pairs in text of documents in a collection of documents, wherein determining the plurality of attribute extraction patterns comprises: identifying an occurrence of the entity and the candidate attribute identified by a first entity-attribute pair in a first sentence from a first document in the collection of documents; generating a candidate lexical attribute extraction pattern from the first sentence; generating a candidate parse attribute extraction pattern from the first sentence; and selecting the candidate lexical attribute extraction pattern and the candidate parse attribute extraction pattern as attribute extraction patterns if the candidate lexical attribute pattern and the candidate parse attribute extraction patterns were generated using at least a predetermined number of unique entity-attribute pairs; and applying the plurality of attribute extraction patterns to the documents in the collection of documents to determine entity-attribute pairs, and from the entity-attribute pairs and the class-entity data, for each of one or more entity classes represented in the class-entity data, attributes possessed by entities belonging to the entity class.
Rather than making this post just the claims of this patent (which are worth going through if you can tolerate the legalese), I’m going to pull out some information from the description which describes some of the implications of the process behind the patent. This first one tells us of the benefit of crowdsourcing an ontology, by building it from the queries of many searchers, and how that may mean that focusing upon matching keywords in queries with keywords in documents becomes less important than responding to queries with answers to questions:
Extending the number of attributes known to a search engine may enable the search engine to answer more precisely queries that lie outside a “long tail,” of statistical query arrangements, extract a broader range of facts from the Web, and/or retrieve information related to semantic information of tables present on the Web.
This patent provides a lot of information about how such an ontology might be used to assist search:
The present disclosure provides systems and techniques for creating an ontology of, for example, millions of (class, attribute) pairs, including 100,000 or more distinct attribute names, which is up to several orders of magnitude larger than available conventional ontologies. Extending the number of attributes “known” to a search engine may provide several benefits. First, additional attributes may enable the search engine to more precisely answer “long-tail” queries, e.g., brazil coffee production. Second, additional attributes may allow for extraction of facts from Web text using open information extraction techniques. As another example, a broad repository of attributes may enable recovery of the semantics of tables on the Web, because it may be easier to recognize attribute names in column headers and in the surrounding text.
Answering Queries with Attributes
I wrote about the topic of How Knowledge Base Entities can be Used in Searches to describe how Google might search a data store of attributes about entities such as movies to return search results by asking about facts related to a movie, such as “What is the movie where Robert Duvall loves the smell of Napalm in the morning?” By building up a detailed ontology that includes may facts can mean a search engine can answer many questions quickly. This may be how featured snippets may be responded to in the futured, but the patent that describes this approach is returning SERPs filled with links to web documents, rather than answers to questions.
Open Information Extraction
That mention of open information extraction methods from the patent reminded me of an acquistion that Google made a few years ago when Google acquired Wavii in April of 2013. Wavii did research about open extraction as described in these papers:
- Open Information Extraction
- Open Information Extraction: the Second Generation (pdf) by Oren Etzioni, Anthony Fader, Janara Christensen, Stephen Soderland, and Mausam Ollie
- Open Information Extraction Software
- Open Language Learning for Information Extraction (pdf), by Mausam, Michael Schmitz, Robert Bart, Stephen Soderland, and Oren Etzioni
A video that might be helpful to learn about how Open Information Extraction works is this one:
An Ontology created from a query stream can lead to this kind of open information extraction
Semantics from Tables on the Web
Google has been running a Webtables project for a few years, and has released a followup that describes how the project has been going. Semantics from Tables is mentioned in this patent, so it’s worth including some papers about the Webtables project to give you more information about them, if you hadn’t come across them before:
- WebTables: Exploring the Power of Tables on the Web
- Recovering Semantics of Tables on the Web
- Applying WebTables in Practice
Query Stream Ontologies
The process in the patent involves using a query stream to create an ontology. I enjoyed the statements in this patent about what an ontology was and how one works to help search. I recommend clicking through and reading the description in the patent along with the Biperpedia paper. This really is a transformation of search that brings it beyond keywords and understanding entities better, and how search works. This appears to be a very real future of Search:
Systems and techniques disclosed herein may extract attributes from a query stream, and then use extractions to seed attribute extraction from other text. For every attribute a set of synonyms and text patterns in which it appears is saved, thereby enabling the ontology to recognize the attribute in more contexts. An attribute in an ontology as disclosed herein includes a relationship between a pair of entities (e.g., CAPITAL of countries), between an entity and a value (e.g., COFFEE PRODUCTION), or between an entity and a narrative (e.g., CULTURE). An ontology as disclosed herein may be described as a “best-effort” ontology, in the sense that not all the attributes it contains are equally meaningful. Such an ontology may capture attributes that people consider relevant to classes of entities. For example, people may primarily express interest in attributes by querying a search engine for the attribute of a particular entity or by using the attribute in written text on the Web. In contrast to a conventional ontology or database schema, a best-effort ontology may not attach a precise definition to each attribute. However, it has been found that such an ontology still may have a relatively high precision (e.g., 0.91 for the top 100 attributes and 0.52 for the top 5000 attributes).
The ontologies that are created from query streams expressly to assist search applications are different from more conventional manually generated ontologies in a number of ways:
Ontologies as disclosed herein may be particularly well-suited for use in search applications. In particular, tasks such as parsing a user query, recovering the semantics of columns of Web tables, and recognizing when sentences in text refer to attributes of entities, may be performed efficiently. In contrast, conventional ontologies tend to be relatively inflexible or brittle because they rely on a single way of modeling the world, including a single name for any class, entity or attribute. Hence, supporting search applications with a conventional ontology may be difficult because mapping a query or a text snippet to the ontology can be arbitrarily hard. An ontology as disclosed herein may include one or more constructs that facilitate query and text understanding, such as attaching to every attribute a set of common misspellings of the attribute, exact and/or approximate synonyms, other related attributes (even if the specific relationship is not known), and common text phrases that mention the attribute.
The patent does include more about ontologies and schema and data sources and query patterns.
This is a direction that search is traveling towards, and if you want to know or do SEO, it’s worth learning about. SEO is changing, just as it has many times in the past.
I’ve also written a followup to this post on the Go Fish Digital blog at: SEO Moves From Keywords to Ontologies and Query Patterns
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Today’s customers have incredibly high expectations for personalized and relevant experiences from brands. That’s why Google Analytics keeps working to better measure the full customer journey in all its complexity.
Let’s look at four new Analytics features that are all about helping businesses understand users so they can deliver more personalized site experiences.
Focus on your users in reporting
Analytics standard reports have been updated to focus on your users. User metrics are an essential way to understand engagement with your customers, especially those who may have multiple sessions across multiple days.
With our updated standard reporting, you can see immediately, for instance, how many users are coming to your site from paid search ― in addition to seeing the number of sessions.
To enable this update, sign in to your account and go to Admin > Property Settings and then choose the toggle switch labeled Enable Users In Reporting.
For other ways to analyze by user, try existing reports like Active Users, Cohort Analysis, and Lifetime Value. In case you’re wondering, session metrics will continue to be available in standard reporting ― that’s not changing. Learn more about audience reports.
Measure lifetime metrics and dimensions for every user
Another tool that marketers can use to analyze visitors on an individual level is User Explorer. And now we’ve added something new: lifetime metrics and dimensions for individual users (based on the lifetime of their cookie). These new metrics and dimensions will give Analytics users a much more detailed way to measure visitors and customers.
For example, you can look back and see the total amount of time an individual user has spent or the total number of transactions an individual user has made on your website. You’ll also see new dimensions that show data such as when a user made their first visit to your site and which channel acquired them.
The new lifetime metrics and dimensions are already available in your Analytics account. Learn more about User Explorer.
Audiences in reporting
For marketers who live and breathe audiences ― which is most of us ― the breathing just got easier. We’ve added the option to publish any audience to a new report in Analytics that should help make every audience easier to understand.
You can now go to the new Audiences report and see a cross-channel view of the audiences you’ve created in Analytics. This is a change from the past, where you could create audiences in Analytics and export those audiences to other products like AdWords, but you weren’t able to publish audiences to Analytics for reporting.
For instance, you might decide to publish an audience to Analytics so that you can see all users who have purchased within the last 12 months but not during the last 2.
You can find the new Audience report in your Analytics account. Learn more about Audiences in reporting.
Reach users most likely to convert
Meet our newest metric: Conversion Probability. It takes user-based metrics one step further to show you just what the name suggests: the probability that a given user will convert in the future. The calculation is based on a machine learning model that learns from users who have made transactions in the past.
The advantages are clear: Marketers can create remarketing lists that target users who have a high likelihood to purchase and then reach those users through either advertising campaigns in AdWords and DoubleClick or site experiments in Optimize.
We are also adding a new Conversion Probability report. This report will show you the Conversion Probability for all your users, including across important dimensions such as channel.
This new feature from Analytics Intelligence is the first forward-looking estimate of how likely a conversion is for individual users. It’s rolling out in beta to all Analytics accounts over the next few months. Learn more about Conversion Probability.
These four new enhancements will help you better understand your users and what they are doing on your site, so that you can create better experiences for them. If you — like those 90% of marketing executives — are working hard to understand your users’ journeys, we hope you’ll find these features useful.
1“The Customer Experience is Written in Data.” Econsultancy and Google, May 2017.
Posted by Gene Chan, Product Manager, Google Analytics
In this new live webinar, Kristin Vick from Hanapin Marketing and Jeff Sauer from Jeffalytics discuss how marketers can ensure they have the budget they need to be effective with online advertising and get the right tools to make that argument.
Read more at PPCHero.com
Explore 4 ways to drive diverse thinking in your company and experience why diverse think is greater than groupthink.
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Chances are that some data is “hidden” in silos across your company. According to new research from Econsultancy in partnership with Google, 86% of senior executives agree: eliminating organizational silos is critical to expanding the use of data and analytics in decision-making.1
If teams don’t talk, or if your organization doesn’t have an integrated data strategy to harness marketing, customer, and advertising data, information and ideas won’t flow freely. Here are three ways to break down data silos and get your organization on the path to a more collaborative, data-driven culture.
1. Make data accessible — to everyone
If you have work to do to get your data house in order, you’re not alone: 61% of marketing decision-makers struggled to access or integrate data they needed last year.2
The first step to making data more accessible is to outline a data strategy that identifies data owners and key points of contact for each information source. Next, define how to integrate data and related technologies, and provide standards and processes related to data security and privacy. Include guidelines for sharing data internally.
Democratizing access to data and insights enables employees at all levels to check their gut — and that leads to better results. The same Econsultancy study found that marketing leaders are 1.6X as likely as their mainstream counterparts to strongly agree that open access to data leads to higher business performance.3
Watch our on-demand webinar featuring new research and best practices in marketing data and analytics strategy from Google and MIT Sloan School of Management.
2. Champion the value of data-driven insights over gut feelings
Once data is made available to marketing managers and business decision-makers, make sure you champion a data-first mindset with your team. Using data effectively is a key differentiator for marketers who are ahead of the curve.
While a documented data and analytics strategy can provide a guide for all employees, support from the top helps set the tone. Nearly two-thirds of leading organizations say that their executives treat data-driven insights as more valuable than gut instinct.4
C-suite buy-in and other champions across the company help reinforce a data-driven culture by giving teams stuck in silos a nudge to collaborate and share analytic insights. Even better, this environment should give teams the incentive to align or share goals since data is core to campaign plans and marketing strategy.
3. Educate stakeholders on how to interpret the data
Having access to data is great, but if employees don’t know how to use it, the insights will remain isolated and unused. Consider this: 75% of marketers agree that lack of education and training on data and analytics is the biggest barrier to more business decisions being made based on data insights.5
If a team is empowered with the right learnings, it will proactively integrate data rather than push it aside. Set up brown bag sessions or internal trainings, or provide employees access to self-paced learning modules.
Finally, consider pairing the “data evangelists” and data storytellers within your organization with different team members to identify areas of focus based on relevant business goals and the biggest opportunities.
1, 3, 4, 5 Google/Econsultancy, “The Customer Experience Is Written in Data”, U.S., n=677 marketing and measurement executives at companies with over $ 250M in revenues, primarily in North America; n=199 leading marketers who reported marketing significantly exceeded top business goal in 2016; n=478 mainstream marketers (remainder of sample); May 2017. 2 Google Surveys, U.S., “2016–2017 Marketing Analytics Challenges and Goals,” Base: 203, marketing executives who have analytics or data-driven initiatives, Dec. 2016.
2 Google Surveys, U.S., “2016–2017 Marketing Analytics Challenges and Goals,” Base: 203, marketing executives who have analytics or data-driven initiatives, Dec. 2016.
Posted by Casey Carey, Director of Platforms & Publisher Marketing, Google
With the rules changing again on exact match, here are 3 ways to adjust your campaigns to alleviate changes to the keywords and search terms reports.
Read more at PPCHero.com