From SPIFFs and President’s Club to new tech gear and all-expenses-paid vacations, sales leaders have many options for motivating their teams. But most of these options are fairly pricey.
Individual reps are also often motivated by different things -- maybe half your sales team would be excited to win a new tablet, while the other half would rather score a coveted parking spot near your office’s entrance.
The answer: Introduce some creative, low-cost rewards into your incentive strategy. For example, you could hand-deliver breakfast to the contest winner every morning for a week. Another prize might be a coupon for a 30-minute nap in the conference room.
Your reps will love the novelty of these awards -- and you won’t have to break the bank. For 13 more ideas, check out this infographic from LevelEleven.
Consulting firms have the highest share of employees who go on to become chief executive officers of American companies.
That's according to a recent report from OnDeck. The researchers used LinkedIn data to determine what proportion of employees at major companies end up as CEOs.
An infographic (below) covers key findings from the report.
The researchers found that McKinsey & Company produces the highest share of CEOs (7.1% of former employees now hold the title). It is followed by Bain & Company, Boston Consulting Group, and Kearny.
Check out the infographic to see the 20 companies that topped the list:
Are enterprise marketers using artificial intelligence to help with their email campaigns? If so, what are the most common uses and challenges?
To find out, RPE Origin and Ascend2surveyed 110 marketers who work for organizations with 500+ employees.
Some 24% of respondents say they're already using AI extensively in their email marketing campaigns, 33% say they're using it to some extent, 25% are not using it yet but have plans to, 14% have no plans to, and 4% are unsure.
Among enterprise marketers who are using or plan to use AI as part of their email campaigns, 50% are doing so for content personalization, 47% for email retargeting, and 47% for subject line optimization.
Respondents say the top challenges they've faced when implementing AI in email marketing have been data/customer privacy concerns (45% cite), data quality or availability issues (42%), and a lack of internal expertise (40%).
Some 45% of enterprise marketers believe AI will play a central role in email marketing in the future, 47% believe it will have a significant impact but won't replace human creativity, 5% believe it will have limited applications, and 3% are not sure.
About the research:The report was based on data from a survey of 110 marketers who work for organizations with 500+ employees.
Which generative AI tools should you be considering?
For B2B marketers, this question has become increasingly difficult to answer as more and more solutions have come to market.
To help sort through the many options available, TopRank Marketing created an infographic (below) that covers more than 30 of the best generative AI tools for B2B marketers.
It looks at top solutions across a range of different areas, including visual content creation, text content creation, and SEO.
Employees and job seekers say micromanagement is the biggest red flag in a workplace.
That's according to a survey of workers conducted by Monster in August, 2023.
An infographic (below) summarizes key findings from the poll.
Some of the other workplace red flags identified by the survey respondents are having too many rounds of interviews for a position and seeing favoritism from managers.
Using capability maturity models to audit your digital maturity and set targets to improve digital marketing effectiveness
We've been adding to our visual tools to help all members assess how well their businesses are adapting to using digital media and technology and to set targets to improve their results from digital marketing.
We have collected these visuals together in a single download so that you can easily review them and print the most relevant for you. We've designed them So members can use them for different scales of business and roles. There are more than 10 templates which cover:
Digital marketing for small and medium businesses using our RACE framework
Digital transformation for larger businesses
Digital channel marketing activities including SEO, Social media, email and content marketing
You can see one example, which I designed for reviewing digital marketing effectiveness with senior leaders in small and medium or larger businesses. The other templates are more granular looking at specific digital marketing activities using our RACE framework.
Members can also use our Capability graders, which are free, interactive versions enabling you to compare your score to other members (anonymously) and get recommendations on which resources can help you improve your score.
Of course, capability graders and improvement recommendations are most useful to businesses that are actively trying to improve their digital marketing strategies. If you're still looking for buy-in for digital marketing activities or optimization, you could start by reading up on '10 reasons why you need a digital marketing strategy'.
What is the 5 point benchmarking scale based on?
In this article I'll explain the background to these capability reviews - I have to go back a while since I first became aware of the benefits of doing this type of process benchmarking back in the early 1990s!
Do you know the Carnegie Mellon Capability Maturity model (CMM)? That's where my inspiration for benchmarking businesses for digital marketing originally came from. It’s likely that you don’t, if you work in marketing, unless your background is similar to mine.
I used to manage software development back in the day, before the web, yes that long ago…
Back then I used to manage small teams to create packaged software used by thousands of engineers worldwide, so it was important that we minimized defects when we shipped a new release. Of course, every major bug irritates customers and generates support and rework.
So the team leaders and I worked hard to implement a quality management system process for creating new software updates to minimize bugs - many who are involved with managing updates to web and E-commerce sites will be familiar with requirements specs, prototypes, and testing schedules, although this was before Agile and Scrum.
As part of trying to improve our development processes, we used to find it useful to apply capability maturity models to benchmark against competitors. They help you be more objective about your capabilities and know where improvements are needed. In the classic CMM model there are 5 or 6 clearly defined stages as shown below:
The story behind developing these digital marketing maturity benchmark tools
When I switched from software development to marketing to lecturing in the business school in the University of Derby around 1995, the web was in its infancy and there were a lot more problems with managing site performance and content than there are today. Remember those quaint “under construction” signs. Laughable now!
Many managing the adoption of digital technologies by their companies were based with a similar problem to the software developers. They needed to develop a robust, repeatable process that would enable them to deliver a service that was effective both for their customers and their commercial goals. Many still do. So this is where reviewing your capabilities can help.
If you're new to digital marketing, don't forget to check our top 18 recommended digital marketing techniques by asking 'what is digital marketing?'.
Using benchmarking or scoring of your digital maturity can help:
1 Audit current approaches to digital marketing to identify areas for improvement;
2 Benchmark against competitors who are in the same market sector;
3 Identify best practice from more advanced adopters;
4 Set targets and develop strategies and roadmaps for improving capabilities through time;
5 Communicate the current situation to colleagues budget holders and highlight investment priorities in for different activities.
This need for well-managed processes is still the case, particularly with ongoing developments in the technology for delivering customer experiences across mobile and desktop and the need to integrate content and social media from multiple sources. Given that digital marketing is “Always-on”, it makes sense to benchmark the overall capability of digital marketing using a simple scoring system.
I used to participate in Workshops at Cranfield School of Management where capability models developed by Professor Hugh Wilson were reviewed with companies participating in a benchmarking group. This rang a bell, so it gave me the idea to apply what I had learned of CMM for software development and apply it.
Benchmarking frameworks for Smart Insights Business Members
I originally developed capability benchmark spreadsheets on personal consulting projects for brands like Barclaycard, BP and Mercedes Benz where I interviewed stakeholders asking them to assess their digital capabilities on a detailed scale.
A version of this was referenced later in the Econsultancy Managing E-commerce Teams reports I worked on in 2005 and 2008 and more recently have updated them to the Smart Insights Digital marketing strategy audit which is structured around the RACE Planning framework - it's where we recommend Expert members start their improvements to digital marketing.
We also have an online retail capability benchmarking audit by Chris Jones. I got in touch with Chris since I admired the auditing approach in his Multichannel Retail Handbook and we arranged to share it with Smart Insights members.
Free Interactive Benchmarking tool
After developing many digital benchmarking spreadsheets and marketing strategy audits, I wanted to take digital benchmarking to the next level by having an interactive tool that could be used to score a business digital marketing capabilities and make recommendations to improve.
That's what our interactive digital strategy benchmarking tool does. By scoring your business capabilities across all areas of the RACE planning framework you will be given a score and recommended resources and e-learning models to help you improve your business capability to use digital marketing effectively.
Continuous adjustment of strategy in the light of changing organisational context has been a recurring theme in my last three posts. This post offers something of a footnote to the earlier articles.
The header image above provides a slightly different perspective on the ever-shifting point of decision. The time dimension is emphasised here in a way that was less obvious in the previous schematics. Your monitoring of past performance is helpful in evaluating options in the present moment, to determine future goals, policies and actions (directing).
As your non-profit board recognises changes in both your stakeholder needs and the environment in which your organisation is operating, the answers to the key strategic questions also evolve. Reiterating messages from my previous post, those questions are:
What should we do, and why? (Strategy)
What could go wrong? (Risk)
Consideration of these two closely related queries will desirably include answers to ancillary questions, such as:
What if we did nothing?
What might we need to stop doing if we are to allocate adequate resources to the new strategic initiative/s?
Previous points of decision and their strategy outcomes are suggested in this chart by the transparent discs located on the ‘past’ segment of the timeline. While earlier decisions were relevant for the prevailing circumstances at the times they were taken, continuing relevance cannot be assumed given changes in stakeholder needs and the world around us.
Documented policies and strategies help us to remain focused on our purposes, but effective governance can only be achieved in the present moment. A steady hand on the tiller is fine in calm waters, but a storm (e.g. COVID-19) demands different navigation skills. Adaptive leadership, strategy, and governance require accommodation of new priorities, and de-prioritisation of goals and initiatives that are no longer relevant.
Network effects are defined as a phenomenon in which as more people or participants join a platform, the value of the platform and the service it offers rapidly increases.
The core idea of a network effect is that each additional user increases the value of a good or service.
Digital giants like Facebook, Uber, Google, and Amazon all harness the power of network effects.
What they all have in common is that they are platform businesses.
The concept of how a network effect works can seem difficult at first but it is really quite simple.
By understanding network effects, platform designers can plan how to rapidly scale and grow a platform. It is because of the network effect that growth marketing techniques are adopted.
There is no one definition, but a good overall explanation is below:
The two key points in this definition are that it isn’t just about adding users but rather users and usage determine a positive network effect.
A network can be large or small. Before the internet is hard to imagine a place where millions of people could easily find others. Digital technologies and the internet have radically changed and provided the backbone for a global network of interconnected networks.
What is more remarkable though has been the rise of the platform economy. More and more companies have seized on the properties that networks provide to create platforms that offer services, connect people and achieve incredible reach and scale.
There is hardly any industry that hasn’t been affected by platforms. The digital technologies that enable platforms have created a vast number of new services many of which have dramatically disrupted industries. The print industry e.g. newspapers have barely survived and those that have had to adapt to a new digital world.
But although we talk about platforms it is the network effects that provide the fuel for creating value.
Why Networks Effects Are So Powerful
As pointed out by James Currier in the NFX Bible, data indicates that network effects (NFXs = Network Effects) are responsible for 70% of the value created in technology since 1994.
In fact, if you look at the companies that arecurrently valued at over $1 billion, most of them are platforms. There are over 400 and counting. Technology, automation, access to information and connectivity are driving the shift to platforms.
The term platform has generically been associated with network effects. There are several characteristics and types of platforms and NFX’s associated with them. I’ll break it down so we can an overview and understand how it all fits together.
Not all network effects are the same, however, and understanding them is essential for building network effects of your own into your products.
Different types of effects are stronger or weaker than others, and often they work differently.
Network Basics
If you have any interest in creating or running a platform you need to understand the NFXs that you’re subject to. You should also be looking to distinguish network effects from counterfeits and how to activate them and make them more robust.
Network effects – density and information flow
A network in its basic form consists of nodes and then connections. Nodes can be things or people. In most cases in platforms its people.
Some examples:
Unidirectional flow – an influencer on Twitter who tweets out but doesn’t respond to anyone.
Bidirectional – two people chatting using Facebook messenger.
The Laws of Network Effects
The laws that determine network effects
Research into networks and platforms has grown exponentially.
However, there are some notable laws that have been foundational in understanding networks.
Sarnoff, Metcalfe and Reed’s have provided simple fundamental laws that describe how network value grows in relationship with the network size and shape.
The so-called Network Effects Laws are not immutable. Some effects do not neatly fit these laws but these laws still provide some useful ways to understand networks.
Network Effects Are Not The Same As Virality
Network Effects
Viral Growth
NFX are about monopoly, hacks and retention and building moats – a defensible competitive position.
Is about speed of adoption, getting as many as possible as quickly as possible. Is about cashing in.
Product becomes more valuable as more people use it.
Product/service may cost less to produce (economy of scale) but it does not become more valuable as more users use it or join the platform.
With more users there are often better engagement, usage rates, lifetime value (through feedback loops) and therefore margins.
The usage rates or engagement does not increase the customer lifetime value.
Network scales faster as it lowers its customer acquistion cost (pull).
The cost of customer acquisition remains.
Network Effects are Not Economies of Scale
Economies of Scale arise when there’s sufficient volume of production to massively reduce the costs, so the largest player can maintain the best margin of profitability. Good examples of this include, Apple’s production of their phones.
Network Effects are distinct from Economies of scale because they produce greater value for the marginal increase in cost. As Networks grow larger, the cost increases, but the value of the product increases faster.
Types of Network Effects
There are different types of Network Effects that affect how a platform operates and its application.
Developing a platform strategy requires an understanding of network effects as well as network externalities. These two terms are often confused because they are both associated with the design of a platform.
If you want to understand the differences, because they are not the same, and they don’t create the same results. This short breakdown from the Stern School of Business (NYU) defines the types of Network Effects:
Direct network effect
A characteristic shared by many digital platforms is that they benefit from direct network effects. This means that a platform becomes more attractive for users as the total number of users on the same side of that platform grows.
Examples of platforms that benefit from direct network effects include social networks and communication applications like Facebook, LinkedIn, WhatsApp, and Skype.
Indirect Network Effect
Indirect networks effects are where an increased usage of one product or service also results in the generation and use of valuable complementary goods.
As the complimentary product use increases the use of the original product or service also increases.
As an example, the direct network effects associated with the Windows operating system results in the indirect networks effects for Microsoft Office. The more people that adopt and use Microsoft operating system grow the install base for MS Office.
A further example, the Amazon business model harnesses the power of both direct and indirect network effects.
Two-Sided Network Effect: Platforms
Network effects can also be two-sided: increases in usage by one set of users increases the value of a complementary product to another distinct set of users, and vice versa. In many cases, one may think of indirect network effects as a one-directional version of two-sided network effects.
This is the type of Network Effect that defines Marketplaces such as Airbnb and Uber (see the Uber Business Model). The more riders you get doesn’t improve the Uber experience but it does attract more drivers, which will improve Uber for me.
Local Network Effect
The microstructure of an underlying network of connections often influences how much network effects matter.
For example, using instant messaging each user is influenced directly by the decisions of only a small subset of other users — those they are “connected” to.
negative network
On the flip side, though, the fewer people who use Venmo, the less valuable it becomes — you can only send money through the app to a limited amount of people. The lack of people using Venmo would make the mobile payment service lose even more users.
Network Effect From Concepts To Practical Use
An explanation of the main network effects
There are many other factors that contribute to the network effect. As platforms and networks grow platform owners often change the way the platform works.
As an example, Amazon initially was a platform that allowed people to easily buy books. Now they sell millions of products and also have an advertising platform. These factors affect influence, contribute and reinforce the value growth with size and participation.
These value drivers are called “reinforcing” mechanisms. As an example, the more data is available on a network, the more the platform can train an AI system to be able to become prescriptive and help the participants navigate the complexity of the network or execute particular strategic actions.
Critical Mass & the Chicken-and-Egg Problem
Platform growth and critical mass
Critical Mass is often described as the number of participants or size of the network needed to allow the platform itself to auto-generate its own growth.
Critical Mass can be understood as a tipping point or threshold where a notorious change in the trajectory of a growth curve occurs and a significant increment in the value of the network happens. Critical mass is a concept of achieving liquidity (a concept overlapped with critical mass).
Platform growth and the chicken and egg problem
For early-stage networks, reaching this point becomes existential, especially for two-sided marketplaces where is important to attack both sides of the network. Often referred to as the Chicken-and-Egg problem is the endless chase to find out how to kick-start the flywheel when you need multiple sides of the ecosystem to join and interact through the platform.
Network Properties
Network properties
To understand which mechanisms will impact positively the growth of our network-platform and will facilitate reaching critical mass, we need to know deeply how the nature and behavior of the participants interrelate with the Value Proposition perception of the Platform. In particular, there are five essential aspects we’d like to focus on.
Example of an evolving platform Airbnb
Commoditized or Differentiated Supply
How easy is it to replicate the offering among the suppliers? This is a key question to ask. As an example while Airbnb hosts offer places to stay that are partially unique (at least in terms of position, if not vibe or host reputation) — and this is increasing with experience hosting — Uber drivers are easily replicable since the value one gets is that of getting from A to B.
Symmetry or Asymmetry of the Supply / Demand
Most of the networks have asymmetric weights of their supply and demand Asymmetry can offer a good hint in choosing what side one should attract first (we’ll talk about this later in more details)
Flexibility of Location: Locally or Globally Bound
The supply and demand of a network are also more or less attached to a certain location. Sometimes networks are highly local, other times demand (or supply) can consume or serve a more larger network.
Single Tenancy or Multi-tenancy
Multi-tenancy is when participants from the supply and/or the demand side may tend to juggle on multiple platforms to get out most of the value.
Transaction Frequency and Lifetime
Another key property of networks is the frequency of the transactions. In multi-sided systems, transaction frequency might vary — depending on what relationship we’re focusing on and to what sides of the system.
Conclusions and Further Considerations
Technology has become democratized – low cost, accessible, modular and built with ease of integration. Mastering the basics of network effects is an increasingly important skill, independent of your intention, goals or priorities. It is though a subject that is part of my research in the design and development of value in platforms and ecosystems.
