Top brand and data-driven marketing

Find out how top brand execs lead their organization into the modern world of data-driven marketing. Strategies are built on insights pulled from the analysis of big data, collected through consumer interactions and engagements, to form predictions about future behaviors.

Data-Driven Transformation: Accelerate at Scale Now

Data-driven transformation is becoming a question of life or death in most industries. But initiatives to embed data in operations throughout a company often fail. This is because companies start by trying to reinvent their core IT systems—a multiyear effort that can run to hundreds of millions of dollars. Sadly, most of this money is wasted, because these massive centralized efforts take far too long. When the rules of business are being rewritten on a quarterly basis, companies need an approach to transformation that is agile, focused on results, and manageable.

Most CEOs recognize the power of data-driven transformation. They certainly would like the 20% to 30% EBITDA gains that their peers are racking up by using fresh, granular data in sales, marketing, supply chain, manufacturing, and R&D. And they may even dream of joining the ranks of data-driven companies that have shoved aside traditional players among the world’s most valuable companies. (See Exhibit 1.)

Yet CEOs are right to wonder how their organizations—where managers and executives already complain about a lack of data skills and where overburdened IT systems seem unlikely to be able to handle a tenfold increase in company data—can pull off such a transformation. These CEOs want to find a reliable way to move their companies into the data-driven future so that they can set up their companies to survive—and not put them in danger in the process.

There is a better way to approach data transformation. In our experience, these initiatives can succeed only if they are cost effective, incremental, and sustainable. Transformations should start with pilots that pay off in weeks or months, followed by a plan for tackling high-priority use cases, and finishing with a program for building long-term capabilities. Working with clients across industries, we have developed a three-phase approach to data-driven transformation. It starts with small-scale, rapid digitization efforts that lay the foundation for the broader transformation and generate returns to help fund later phases of the effort. (See Exhibit 2.) In the second and third phases, companies draw on knowledge from their early wins to create a roadmap for companywide transformation, “industrialize” data and analytics, and build systems and capabilities to execute new data-driven strategies and processes.

This three-step approach is faster, less costly, and more likely to succeed than a system-wide overhaul. Using existing data systematically and combining it with external data (from social networks, for example) for marketing or customer issue resolution can deliver fast results. We have seen companies achieve 15% to 20% of the potential of a full data-driven transformation in six to nine months.

Use quick wins to learn and fund the digital journey. In this first phase, companies identify the low-hanging fruit—discrete, rapid digitization efforts that can deliver quick wins. These projects immediately move the needle on performance in a key area—sales support or supply chain, for example. And rather than taking years, implementation occurs in months and starts paying back almost immediately. The pilot projects show that the company can benefit from digitization, and they provide important lessons in how to roll out digital transformation across the company. Crucially, the extra value that the quick wins create can help pay for longer-term efforts, potentially making the transformation self-funding.

Design the companywide transformation. In the second phase, which can begin while the first initiatives are still underway, the company draws a roadmap for company-wide transformation. This involves building a portfolio of opportunities—identifying and prioritizing functions or units that can benefit most from transformation. It also involves locating and starting to address roadblocks to transformation. During the design phase, companies also invest in framing and communicating the vision for the transformation to build support for needed changes, and they invest in systems to industrialize data analytics—making analytics a resource for every operation.

Organize for sustained performance. With a detailed roadmap in place and with the experience and funding available from the early projects, the company is ready to undertake a full-fledged digital transformation. In this phase, digital and data-driven processes and work methods spread to every corner of the company. Employees learn to work across silos to enable data-driven processes, and leaders make the organizational changes necessary to sustain the new approaches. The company creates a data-driven culture by investing in capabilities to use analytical insights and by launching a change management program to embed new mindsets, behaviors, and ways of working.

USE QUICK WINS TO LEARN AND FUND THE DIGITAL JOURNEY

Moving a big company in a new direction is a huge challenge for management. The best-conceived and most urgent transformation programs—digital or otherwise—are sometimes no match for organizational inertia. This may explain why 70% of publicly announced transformation programs fail to meet the company’s ambition, its timeline for the transformation, or both.

But large organizations can overcome resistance and build the enthusiasm needed for change to succeed if they approach transformation in the right way. By starting a transformation journey with a small number of quick initiatives that demonstrate what can be achieved by using new approaches, companies greatly increase their chances of eventual success.

Leaders should choose quick-win initiatives carefully, on the basis of several critical criteria: they must have a high chance of success, a significant and rapid payback, and visibility across the company. A major industrial company, for example, started by digitizing high-profile processes, including inventory management. (See “Building Momentum Through Pilots.”)

BUILDING MOMENTUM THROUGH PILOTS

A large industrial company contemplated a massive digital transformation to increase its efficiency and to compete more effectively in markets where producers have limited pricing power. The company did not want to tie up capital in a massive change program and wait years for a payback. To avoid that outcome, the company first identified a few quick-win initiatives that could pay off within a month or a quarter.

The first initiatives it selected were in inventory management and capacity optimization—analyzing output and shifting production to sites that made the most profitable products. For these quick wins, the company used static data and created one-off solutions. But the projects led to significant savings and more sales of high-profit items, which generated immediate value. In nine months, quick wins generated $20 million in value. Once the projects based on static data were up and running, the company went back and built the systems it needed to manage these processes and functions continuously, using real-time data flows.

Applying the lessons from its early wins, the company has created a roadmap for ten major data transformation initiatives in areas as varied as demand forecasting and managing the outbound sales force. The company has also made plans for new companywide resources to support data-driven approaches and make them sustainable, including building a data lake. And it has begun identifying new data-driven business models. The overall goal for the transformation is to unleash $200 million in value over three to five years and to help the company raise its EBITDA margin by 2% to 4%.

Initial projects may be limited in scope, but it is essential that they succeed and serve as a convincing advertisement for the benefits of digital transformation. For this reason, companies should not only choose projects carefully but also be pragmatic about execution. It is best to avoid projects that would require fundamental changes in data handling—projects that would entail building a new data repository, for instance. Companies should use agile methodologies to build any new analytics models, with short sprints and tight timelines for developing a minimum viable product that can be tested and used to define additional requirements and refinements.

Quick-win projects should require no more than four to six months to complete, and their value should be demonstrable within weeks. During the quick-win phase, companies can build their ability to focus and execute swiftly and to work across silos—critical capabilities for pursuing large-scale transformation efforts. Quick wins can also energize and inspire managers and employees who have seen change initiatives bog down in the past.

DESIGN THE COMPANYWIDE TRANSFORMATION

As soon as it is clear that the early digital transformation projects are off to a solid start, the company can start preparing the roadmap for extending digital transformation across the enterprise. This starts with a high-level vision, which company leaders translate into a portfolio of initiatives (or use cases) to be rolled out in a logical order, on the basis of factors such as size of impact and competitive needs or opportunities. Then the company must agree upon some underpinnings of digital operations—analytics, data governance, and data infrastructure. (See Exhibit 3.) Creating a roadmap for use cases and projects to build data infrastructure and other resources needed for data-driven operations can not only make the transformation run more smoothly but also ensure that these investments pay. (See “Driving Fast Value from Data Transformation in Logistics.”)

DRIVING FAST VALUE FROM DATA TRANSFORMATION IN LOGISTICS

For more than 30 years, a major global logistics company led its industry in the use of information technology, and its leaders believed that data was its competitive differentiator. The company took the long-term view and had spent more than five years implementing a new ERP system that cost hundreds of millions of dollars. But after massive investments of time and money, the company could not demonstrate that it had gained any competitive advantage in cost or revenue.

Then the company took a new, agile tack. This time it created a detailed roadmap for transformation based on two primary considerations: an examination of the data needed monthly, weekly, and in real time to optimize functions or operations and to generate the most impact for the company; and an assessment of the systems and data already available to fill the newly identified business needs. On the basis of this roadmap, the company began a series of pilot projects, using benchmarking data to optimize important cost drivers such as fuel consumption, maintenance, and labor. Another project aimed to improve pricing performance by accessing data such as customer P&L through new analytics.

Over the course of three years, the company systematically completed the list of projects for every major value driver in the business. After dozens of projects in areas such as pricing, fuel consumption, and network, the company went from sitting in the middle of the pack in operating performance to becoming the industry leader on EBIT performance.

Before attempting to define its vision, a company needs to have a thorough understanding of where it stands in terms of data, digitization, and current capabilities. As a preliminary step, then, the company should quickly and objectively assess its situation and gauge how its capabilities stack up against best practices in its industry. One option in this area is a diagnostic developed by BCG that weighs 21 factors in assessing a company’s starting point in data and analytics capabilities and assets, backing up the assessment with extensive, continually updated benchmarks.

Five Critical Steps for a Successful Data Transformation

The assessment of data capabilities gives the company the information it needs to carry out five critical steps.

Build a vision. When planning a data-driven transformation, a company must set the appropriate vision for its business. For some companies, the transformation will mostly be about using data to improve operations and to compete more effectively. For others, it might involve building new business models. The visioning exercise should include identifying the macro use cases—the most important projects that the company wants to undertake.

Select the portfolio of initiatives. Using its vision and its list of macro projects for reference, companies can create a full list of transformational initiatives. The company should use a structured ideation process to compile the list, and it should use a rigorous prioritization methodology to set the schedule. Factors such as data availability, regulatory compliance, and technical or modeling difficulty, as well as dollar value, customer benefits, and strategic importance must also be weighed.

Devise an analytics operating model. Before investing in new data analytics capabilities, a company should specify how it wants the data analytics function to work. After analyzing its internal capabilities, it can decide which components of the analytics function to seek in-house and which to outsource.

Establish data governance. To ensure the quality and integrity of the data it will use for business decisions—with and without human intervention—a company must have strict governance rules and a data governance structure. It must also define data quality and establish ways to continually improve it.

Define data infrastructure. A company moving toward data transformation should address the following questions: Can our current infrastructure support our future data value map? Should we make or buy? Should we go to the cloud? Do we need a data lake? What role should our legacy IT systems play in our data transformation? The company should design a data platform (or data lake) that can accommodate its product map and should use that platform to progressively transform its legacy systems.

Industrialize Data Early to Ensure Full Transformation and Long-Lasting Impact

While the company continues to sketch the transformation roadmap—if not sooner—it needs to begin industrializing its data and analytics. This means setting up a way to standardize the creation and management of data-based systems and processes so that the output is replicable, efficient, and reliable. Digital systems are the new means of production, and they need to have all the attributes of industrial machinery, including reliability and consistency. Above all, the company needs to have a way to guarantee that it generates and harnesses high-quality data and has an efficient data environment.

A centralized or hub-and-spoke operating model can ensure clear, consistent strategy and execution; rationalize investments; and ensure economies of scale. Business units and functions that will rely on new data-driven systems and processes should have input into system design and data quality assurance, but these groups must rely on the core data management organization for data governance.

The second element in industrializing data consists of determining the appropriate architecture to support data analytics across the organization. A flexible open architecture that can be updated continuously and enhanced with emerging technologies is generally the best option. Rather than embracing an end-to-end data architecture, companies should adopt a use-case-driven approach, in which the architecture evolves to meet the requirements of each new initiative. The data governance and analytics functions should collaborate to create a simplified data environment; this will involve defining authorized sources of data and aggressively rationalizing redundant repositories and data flows.

ORGANIZE FOR SUSTAINED PERFORMANCE

As is the case with any change program, the success of a data transformation is measured by sustained results—and those will not materialize without making the company and its culture data centric. To prepare its organization for a digitized future, the company needs to move on four fronts: creating new roles and governance processes, instilling a data-centric culture, adopting new ways of working, and cultivating the necessary talent and skills.

Many companies may be capable of managing this change on their own; but if a company faces competitive challenges that require a rapid transition, or if it is far behind in digitization or lacks the resources and capabilities to manage the transformation, it may benefit from adopting a build-operate-transfer model (which we discuss below). This involves creating a dedicated organization—usually run with the guidance of an outside expert partner—that takes over the organizational change effort.

Define new roles and governance rules. To ensure the sustainability of the benefits it obtains through the adoption of new digital processes, a company needs to make clear who has responsibility for building and running new systems and maintaining specific types of data—and how to manage those people. The changes begin at the top: senior leaders should adopt data-driven objectives and cascade those goals throughout the organization. Top management may want to set up data councils to extend the work to all sectors of the organization and to carry it out more effectively. The company should promote data awareness by using data champions to disseminate data-driven practices. The company can set up a change management function under the chief data officer, too. The data awareness effort should extend to all work, including tasks that the digital transformation does not directly affect. For example, the company might create data-based metrics for functions such as HR, perhaps measuring the number of applications processed per job filled.

Build a data-first culture. Not everyone needs to become steeped in data analytics or learn to code in order for digital transformation to work. However, everyone does need to adopt a less risk-averse attitude. To move quickly and to continually find new ways to apply data, companies should behave a bit like software development operations, embracing a test-and-learn culture that encourages experimentation, accepts—even celebrates—failure, and is always learning. Companies can also encourage the desired cultural change through organizational moves, such as creating internal startup units where employees can focus on experimentation or co-locating data labs within operating units. The company can also promote the new culture by using cross-functional teams that share data across silos, thereby encouraging openness and collaboration throughout the organization.

Adopt agile ways of working. The entire organization does not have to become expert in agile, but the company can adopt many of the tactics of the agile method and use them in everyday operations to increase the organization’s responsiveness and adaptability. It can establish scrum teams with squads and tribes to tackle specific problems—and accelerate the pace with weekly sprints, rather than months-long efforts. Teams and groups can implement morning standups and weekly demos (reviews) as part of governance. Overall, the new ways of working should emphasize autonomy and reduce hierarchy.

Cultivate the necessary talent and skills. For data-based transformation to work, the company must have talent with the right skills to execute data-driven strategies and manage data-based operations. This presents a workforce planning challenge, starting with assessing current employees and defining future needs. The company should create an inventory of the talents and skills that its employees will need, and it should identify where the gaps are in the current workforce. Companies will need to retrain current employees, hire new talent, or use a partnership to get the right capabilities. To recruit people with digital skills, the company may need to rethink the value proposition it offers—work, opportunity, rewards, career path, and so on—in relation to what tech companies offer.

Consider the build-operate-transfer model. In some instances, a company may need to adopt bolder steps to accelerate its data-driven transformation. This may be because it is starting from far behind its competitors or because it lacks the capabilities and resources needed to drive the transformation internally. In such situations, using the build-operate-transfer model makes sense. Adapted from the construction industry, this model involves creating a stand-alone organization in partnership with an outside vendor that has the expertise to run transformation initiatives. The organization focuses on managing transformation efforts and is staffed by employees from both the firm and the outside partner. It takes responsibility for setting up and running the use case projects and other elements of the transformation. Over time, as projects are completed, the partner withdraws its employees. Eventually it transfers all work and resources back to the company, and the stand-alone organization dissolves.

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The promise of data-driven transformation has captured the imagination of leaders throughout the business world and is driving change in the public and social sectors, too. Executives are inspired by the idea of using data to make better decisions and digitizing all sorts of processes to improve performance. They are also motivated by fear that they won’t be able to keep up with competitors who are ahead of them in data-driven digital transformation. These forces can encourage companies to try to achieve sweeping, companywide change to go digital—which can lead to counterproductive overreaching. This contest will not be won by making huge bets. The winners will be agile, pragmatic, and disciplined. They will move fast and capture quick wins, but they will also carefully plan a transformation roadmap to optimize performance in the functions and operations that create the most value, while building the technical capabilities and resources to sustain the transformation.

Antoine Gourévitch , Lars Fæste , Elias Baltassis , and Julien Marx

Artificial Intelligence and Content Marketing in 2017

7 AI Implications for Your Marketing Strategy

Sasha Laferte

Some futurists believe as artificial intelligence (AI) becomes smarter, it will lead to a technological singularity: humans and machines melding into one digital, biological entity. Such an explosion of intelligence would lead to an entirely new way of life.

This prediction isn’t fantastical. Computer scientists forecast that by 2029 AI could be at about the level of intelligence of adult humans. In October 2016 the US National Science and Technology Council released their first ever document on preparing for the future of artificial intelligence. This document outlines the potential impact AI could have on the world economically, including an increase in the wage gap—and significant job reduction.

A quick Google search for “Content Marketing and Artificial Intelligence” reveals how big a concern this is. Top results include: Will Artificial Intelligence Kill Content Marketing?, Will Artificial Intelligence (AI) Take Over Content Marketing? and Is Artificial Intelligence Taking Over Content Marketing?

For now, AI isn’t causing a robot apocalypse, or even taking marketing jobs, although a study at Karlstads University determined that people are not able to tell the difference between content written by journalists and those generated by software. AI does however, hold significance as a content marketing trend for 2017.

As digital behemoths acquire AI companies and start bringing the technology to a wider audience, AI is changing the course of the content marketing industry. Among other things, AI is starting to eliminate the need for manual messaging and segmentation, is optimizing the personalization and automation features of content strategy, and driving predictive lead scoring and data analysis at the click of a mouse.

This article details seven ways AI is impacting content marketing, and how you can adapt for 2017 and beyond.

1. Content Creation

Journalist robots are already creating millions of pieces of basic sports content for the Associated Press, as well as Samsung, Comcast, and Yahoo. This technology can create content at a rate of 2,000 pieces per second, and as it improves, robots will become capable of creating more complicated content. Right now however, there are limited programs available for the average content marketer, but what is available saves time on simpler writing tasks so you can devote more time to more meaningful projects. Some examples include WorldAI, Automated Insight’s Wordsmith, and Narrative Science.

2. Empathy and Personalization at Scale

According to Forrester, 40 percent of loyalty marketers struggle with personalization. Marketing automation software company Emarsys is addressing this challenge, announcing a new artificial intelligence-driven platform (Emarsys AIM) in November 2016 that aims to let marketers focus on one-to-one engagement and personalizationwith their customers at scale. Using over two billion Emarsys unified customer profiles, and enabled by AI that automates the timing, content, and communication channel, AIM removes the burden of operational and execution tasks, allowing marketers to focus more on strategy and content. This technology is already pointing marketers to start thinking of content in terms of how it can be personalized by AI software.

One example is WayBlazer, a cognitive travel platform using IBM Watson’s AI to personalize images, recommendations, and travel insights based on customer data. It illustrates how AI can make content intelligent for your audience, allowing you to focus on quality and more personalized messaging.

3. Easing the Transition From Email to Messaging

With the increasing popularity of messaging apps for the office such as Slack, Yammer, and HipChat, combined with messenger platforms such as Facebook Messenger and WhatsApp, chat might be killing email.

There’s obvious appeal in receiving an immediate, custom answer from a company without dealing with the frustration and wasted time of waiting on the phone for a customer service representative. AI not only allows companies to create steadily better chat experiences at scale, but could allow for automated improvements to the product and business model at hand. Beer company IntelligentX Brewing is employing this tactic with AI that changes the recipe of its beer based on customer feedback given through a bot on Facebook.

Just as email, Twitter, and other mediums have impacted content creation, messaging is too. In the case of instant messaging marketing, company to customer messaging results in a quicker, more personalized content strategy, with a higher percentage of reach, and higher engagement and conversion rates.

4. Recommended Content

The Netflix Tech Blog says 75% of what people watch on Netflix is from an algorithm-generated recommendation. Facebook and Twitter are both investing in AI to help match users to relevant content.

Non-tech brands are starting to implement similar strategies for recommended content. According to artificial intelligence & machine learning think tank AI Business, activewear brand Under Armour is working with IBM Watson to combine “... user data from its record app with third-party data and research on fitness, nutrition etc. The result is the ability for the brand to offer up relevant training and lifecycle advice based on aggregated wisdom.”

Companies looking to create their own recommendation engine should investigate machine learning software such as Seldon, or software with a prepackaged recommendation engine such as Apptus, Clerk, or RichRelevance.  

5. Image Analysis

Facebook, Amazon, and Apple have all acquired image recognition AI software of some kind recently (Faciometrics, Orbeus, and Emotient respectively). Audience reaction measurement could open an entirely new way of thinking about how content success is measured and how marketers should engage their audience to interact with content. For example, this technology could enable you to measure the success of your next piece by basing it on how many people laughed or smiled while reading it.

6. Strategy and Data Analysis

IBM’s Deep Blue, a computer designed to beat chess master Garry Kasparov, lost when they battled in 1996. It beat him in the rematch in 1997. Since then, AI has become a commonplace tool to analyze data and inform strategy. Airbnb uses AI to determine how much a host should charge for a stay at their house based on the time of year, location, proximity to holidays and amount other lodging is charging in that area.

Adobe Sensei, launched in November 2016, is an example of how this concept can scale and influence marketing strategy. According to the Adobe website, Adobe Sensei:

... harnesses trillions of content and data assets—from high-resolution images to customer clicks—all within a unified AI and machine learning framework. From image matching across millions of assets, to understanding the meaning and sentiment of documents, to finely targeting important audience segments, Adobe Sensei does it all. Adobe Sensei crunches numbers and notifies you when it finds something interesting. Like a new look-alike audience that you should approach. Or a specific message that will resonate with a customer. And it also offers predictive modeling, so you can anticipate market changes and make better decisions."

Software such as Adobe Sensei has enormous potential to enable you to hone in on strategy and trends that are right for your organization.

7. Customer Success Improvements

Salesforce’s AI machine, Einstein, is geared toward improving customer success. The platform uses “advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery” to optimize for each customer and their interactions with a company’s CRM. The aim is to give content marketers a better understanding of how your content impacts existing customers, and what you can due to pivot your strategy to support customer success.

As AI advances, it is enabling the rapid development of content intelligence to drive content marketing. (Read all about this new wave of content marketing technology here.) Content intelligence technology will move content strategy further away from foggy guesses and closer to exact prognosis, forecasting precisely what to create, and preemptively giving you the analytics you need, right down to predicting the revenue generated by a particular blog post. As computational creativity advances, maybe someone won’t need to predict the future of AI in a few years—AI could already have it covered.

Presentation of information

Presentation of data from market research


Presentation of data is important because it converts raw data into a form that is easier to understand. Information can be displayed as:


Table/tally chart:
It is the most suitable method of presenting data when raw data is needed. However, it offers little more than that and the information should be converted into other forms if it needs to be understood or analysed carefully. It is sufficient for info that is brief or does not contain a lot of different things.



Bar chart:
Charts are a more meaningful and attractive way to present data. They are normally used to compare two or more sets of stats with each other. 


Pictogram:
It is similar to a bar chart but uses symbols instead of columns. It becomes extremely effective if the data is short and simple.


Pie chart:
Pie charts are ways to show the proportion that each components take up compared to the total figure.


Line graph:
Graphs show the relationship between two variables. It can be drawn in a straight or curved line. It is usually to compare things with time and to identify trends.


Alternative ways of presenting information for coursework


Tables
Tables could be also be used to present data in situations such as when people are interviewed on why they like a product and they are given multiple choices.


Photographs
Photos can be used to help illustrate your points or support your work. However, avoid adding them to your work just to make them more attractive. 


Diagrams
Diagrams are used to simplify information. It can be used to show relationships of things which all leads to the same root, which is usually at the centre of the diagram. It can also be used to show variation, e.g. diagram for ways to save water with different ways to do so branching out from the centre of the diagram. 


Maps
Maps are usually used to present location or transport routes, etc… They aim to make the information as clear as possible to the reader. This of course, only applies to certain types of information where words and numbers cannot express them.

Marketing Data: What Matters And What Doesn't

AJ Agrawal ,

 CONTRIBUTOR

Data is big business in marketing. No successful company can escape from the need to track their performance and make decisions based on data. In 2015 marketing analyticsspending increased by 60%, according to the latest statistics. There are many statistics you can pick up, though.

The question you need to answer is what data matters and what doesn’t. You can learn absolutely anything from analytics, but not all of it is relevant.

The Importance Of Data Protection

Data is coveted and you must have a solid plan in place to protect it. Data bandwidth and data protection rates are increasing faster than anyone could have previously predicted. This is because as data becomes such a huge part of companies it’s naturally going to become a target for fraudsters who want the same information.

Your data should be fully encrypted and it should only be available to a select number of people. The consequences of leaked data and successful hack attacks are dire.

The Point Of Marketing Data 

To understand what data you should be looking at, consider what you will be using said data for. There are only two purposes here:

• To create the image of your perfect customers.

• To understand what’s working and what isn’t.

And you’ll notice that one directly links to the other. Once you refine the image of your perfect customer you can better target them. The problem with many companies is they don’t know who their target audience is, at least on a specific level.

Average Total Revenue Per Customer

This is a statistic you can work out by dividing your total revenue divided by the number of customers. This is the average value of every customer to you. If you have products and services at a range of prices, this will tell you whether you either need to upgrade your marketing campaign or change your focus.

You can dig deeper by only including the revenue and customer numbers from certain periods, such as per week or per month.

Percentage Of Converted Leads

Again, this is something you can measure over a specific period. The number of leads is all well and good, but it doesn’t actually mean anything. You can have thousands of leads and end up converting none of them. Look at your total number of leads and divide it by the number of leads you have successfully converted.

This will give you your strike rate. It will tell you two things. Your strategy for converting leads may be off or the leads you are gathering are not the leads that are right for your company.

Percentage Of Repeat Business

 This is exactly the same calculation as above, except you are dividing repeat customers by the total number of customers. You need this piece of data because it’s the only way you can tell how well you are doing with customer relationship management.

Repeat business is the bell that shows you are treating your customers right. Of course, you have to use a period of time that’s right for your niche or you can skew the results.

Cost Of Acquisition

 For this example, you are going to assume that your company operates exclusively online. You are using Facebook ads in order to attract new customers and you want to work out how much it’s costing you to make a conversion.

An easy example is to use Facebook ads for gathering newsletter subscribers. To work out the cost of acquisition you would divide the numbers for how much you spent on Facebook ads and the number of new newsletter subscribers over a specific time period.

You can steadily work on driving this down over time.

Bounce Rates

In regards to your website, make sure that your bounce rates are as low as possible. If your bounce rates are high, this shows that you are bringing in the wrong audience because they are clicking away almost immediately. Remember that bounce rates can be high because of a technical reason, such as your page takes a while to load.

Where Is The Traffic Metric?

 You will notice that of all the big marketing data on here not a single mention of traffic was made. This is because traffic means absolutely nothing in the grand scheme of things. Unless you’re still living in 2001, there’s no point in having lots of traffic on your website.

Instead, you want leads that come in the form of laser targeted visitors. A lot of companies are happy to see traffic levels go down because they know that the people coming to their website are far more likely to make a purchase.

In your view, what are the most important pieces of marketing data you can have?