AI Playbook for Startups and SMB

The AI Playbook for SMBs and Startups

Most businesses in the SMB and startup class, still struggle to grasp the broad definition of AI disciplines, what AI really is to their business, where to get started and what successful execution looks like for their businesses. Simplifying a focus area narrowing these concerns specifically to address your business pressures is helpful to keep team in line with long term business goals and strategies.

There are many AI Playbooks written by renown gurus such as Andrew Ng, meant for Fortune 500 organisations and they do contain some very useful guidance and tips for all businesses. Nevertheless, smaller ventures with limited funding, skills and resources may need to rework tactics to suit business.

Here we present steps to formulate a generic AI playbook meant for small and medium sized businesses, which includes stages to:

• internalise AI definition for the business; 
• outline and prioritise business problems that can be addressed with AI;  
• experiment with ready pre-trained model
• build required skills or internal structures;  
• prepare training and test data; 
• choosing a path between pre trained and custom built model for solutions, 
• and assessing overall impact to businesses performance. 

The generic AI Playbook should ideally incorporate other dimensions unique to your business, industry and commercial eco-system over time, for maximum results.

Define and internalise AI for your business...

AI itself is a broad subfield of computer science. It encompasses many branches of studies including rule based AI and decision trees; machine learning (regression, classification, neural networks/ deep learning, reinforcement learning, supervised and unsupervised learning); robotics; text and speech processing (NLP, NLU, NLG); vision (computer vision, image recognition) and several other applications that still have not left the laboratories. 

AI is a field where one or several of these approaches are used to create machines, agents and models that perceive, learn and adapt to perform various tasks and cognitive function similar to humans.  

The AI domain is practically exploding with hundreds and thousands of new approaches, methods and solution areas in transfer learning (learning from lesser data and smaller models), networks with memory (generalising AI, e.g. DeepMind’s differentiable neural computer), reinforcement learning, training simulations, hardware for training and inference that are rapidly advancing in the research, development and real world application stages. 

Communicate how AI should be perceived in your business....

As such, it's easy for businesses to get lost in an ocean of offerings that comes with vendor specific methods, best practices, strategies, tools and ready use cases. Charting and communicating how AI should be perceived within the business very early on, in your AI journey is crucial to ensure potent utility, informed expectations and performance. 

For instance should AI merely assist workers to perform better at tasks? Should AI considered as an avenue to outsource cognitive functions otherwise performed by human workers ? Or should AI be a form of cognitive technology to help expand ordinary worker's ability to explore meaningful solutions to business problems both creative and logical?

Setting the right internal tone on AI can guide alignment with work culture, strategies, high impact projects, investments, selection of tools and services that can drive AI values across the business.

Define business problems that can be solved with AI

The common misconception in this stage is, data science or business team alone is responsible to outline business problems solvable by AI. 

Every business unit can contribute to this stage including sales, marketing, services and tech departments. Imagine how it will affect your business if tech can reduce 40% of cooling needs in your datacenter or how your customer service or inbound sales department can improve performance by multi-fold with realtime speech to text analysis that prompts them with emotion, sentiment and other useful information during a call with a customer.

Build working team; conduct regular meet-ups; answer business questions around growth hacking, improving or staging customer experience, workforce productivity and innovation; keep a revisable record of issues pressing the business growth (this can be a simple excel sheet with list of items); prioritise problems to solve; assess data availability; experiment, assess skills requirement; select initiatives for executive sponsorship and create projects to execute. 

Assess data availability and prepare training data for the problem

Data sources...

Data can be gathered from within the business, public datasets, procured from external sources (e.g. Image Net, Google Data search) or a mix of these. Identify useful data assets for your business problem, list what data is missing or not available and which data can be excluded from your raw selections. Collect and aggregate sufficiently to fit your model. 

Data preparation is a significant contributor to cost of building AI solutions specifically ML models, aside from high performance computing resources though GPU innovations has brought so much economics since a decade ago.

Clean and transform data for training...

Most ML models requires large amounts of data to train and converge before they can be applied to business. Preparing these data may require domain experts to collect raw data, identify features with strongest prediction power and label them. Though the business problem you are aiming to solve, tools and selected algorithms ultimately influences data preparation approach and mechanisms. 

Data transformation is revisited several times....

It is common for developers to spend 50% or more time preparing and scrutinising data quality and revisit data transformation processes (or feature engineering) several times during the development of the model. 

Split data sets for training, test and validation...

The prepared data is usually split into training set to fit the model, test set to evaluates final model fit on the training dataset and validation set to evaluates model fit on the training dataset while tuning model parameters.

Consider pre-train models

Ready to train models for various use cases...

For common business problems which requires simple recommendations, classifications, regression, clustering, anomaly detection and ranking, look for pre-trained model offerings by public cloud vendors such as Google, AWS, Alibaba and Microsoft. Many large and niche AI vendors now offer pre trained models for other slightly complex solutions to perform predictions, speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

No need to build algorithm or ML models.....

These offerings saves businesses the hassle of building algorithms and models from scratch. Plus it's a great way to get started with applied AI for business with quicker results and fast ROI times without having to reinvent the entire process (the value is in the model outcome not building it, especially for non-tech businesses). 

No need to replicate work for  ML backend .....

The team need only concentrate on preparing data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models/ functions can then be deployed with other production applications and systems through APIs provided by cloud vendors to apply learnings on new data samples without replicating any work for ML backend.

ML and DL frameworks

In the event that the business problem you aiming to solve is much complex and does not have a ready pre trained model available, it is advisable to custom build the model custom build the model on a machine learning and deep learning frameworknof your choice, offered by cloud vendors through their ML-as-a-service offerings.

Frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, comes readily packaged with interface, libraries, tools, pre-built and optimised components to facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

However, it is highly recommended for new teams to get experience from simpler projects involving pre trained models first before venturing into challenging custom building models. It is easy to loose ROI when tinkering with ML or DL models without past experience and benchmarks.

Building AI skills 

When it comes to smaller businesses, building AI skills in-house is really a trial and error exercise to find the right blend of subject matter experts (or the minimal quo) to address AI problems and build solutions. This team's skill level is adjusted according to how they plan to address business problems, for instance with ready models or custom build models.

AI requires programming (e.g. R, Python, Lisp, Prolog, Scala, C, C++ and Java) data science (at least fundamental level) and business domain knowledge at the minimum to obtain some high level results. A strong foundation in mathematical fields such as probability, statistics, linear algebra, mathematical optimisation is necessary if your business wish to develop own algorithms or modify existing ones to fit specific goals and constraints.

Training existing workforce, hiring from other organisations and university grads are some of the common methods to build the right team.

Education sites such as Coursera, EDX, Machine Learning Mastery and many others provide neutral self learning content suitable for both technical and business audience. Content varies from fundamental to advanced level and comes with certification process that can be committed at one's own pace. 

Vendors such as AWS, GCP and Microsoft too have their own academies but syllabus may be skewed towards respective services. AWS and Microsoft are so far the best corporate academies I encountered and complies to many criteria mentioned in Andrew Ng's Fortune 500 AI Playbook in addition to easy to follow content and no fuss lab use (note that this is paid resources).

Assessing impact to business..

Once trained, tested and deployed, weigh the model's impact to your business performance. I use a no fuss method where I categorise what was increased, reduced, created and eliminated over a set period of time.

For instance:

• Did the model INCREASE overall customers value, revenue or profitability ? 
• Did the model REDUCE fraud risk, customer churn or cost ?
• Did the model CREATE new growth engines, data products or differentiators ? 
• Did the model ELIMINATE manual task, blind spots or redundancies ?

These answers are then scored and quantified for best alignment with overall strategic goals and objectives. The data is then collated for all AI projects to visualise and quantify the total impact to business financially (profit, revenue, growth rates, etc) and other areas such as competitiveness, reputation, and customer satisfaction. 

This exercise is helpful in two or three ways :

• to identify successful AI projects and approaches
• to build AI projects that can increase the value or performance of existing models
• find optimal opportunities to apply AI learnings to other functional areas 

There are many ways to measure impact, choose one that you are most comfortable with and put it to work. Use the discoveries to increase effectiveness of AI projects for your business while filling gaps in skill sets and access to useful data assets.

Conclusion - Ethics, Explainability and Human-Machine Interaction

In the end, AI systems are nothing but stacks of infrastructure and programs in various architectures that learns and executes continuously, depending on its utility. The machines aid human workers to predict outcomes, navigate a situation and decide on best action path with less mental drudgery while spanning solution options that was never possible before.

But that doesn't mean AI systems and models are perfect or free from self vested exploitation. Since the 2010s public concerns about racial and other biases in the use of AI for criminal sentencing decisions, creditworthiness and other forms of social credit systems (e.g. China) is driving demand for transparent AI systems and agents.

Explaining decisions of AI algorithms or models, especially those powered by deep learning or other complex neural networks is still a difficult task and is also known as the 'interpretability' problem.  Unlike decision trees and Bayesian networks that are more transparent to inspection, complex neural networks stack on one another and may involve millions of neurones processing towards the outcome.

As such, deploying sufficient governance protocols for data privacy and security; an architecture which facilitates transparency and tools to detect bias in algorithms is as critical as creating successful models or functions. Though in many cases engineering for more explainability and transparency may lead to significant accuracy trade offs. 

Should Government Regulate Ride-Hailing?

#LKminiblog - Should Government Regulate Ride-Hailing?

Indonesia is planning to regulate ride-hailing rates, amid pressure and protest from driver groups. Both Grab and Go Jek depended on low price offers to passengers in the past for initial growth and expansion, but prices have always surged as business matures. Plus, the ride hailing firms subsidises drivers during discount campaigns. 

Low price is just an entry strategy....

The low price was just an opportunistic route to break into new grounds and get customers accustomed to a new alternative. Over time, reliable and consistent service quality became the foundation to sustaining the massive success of these unicorns. 

Ride-hailing businesses run on leading edge technologies, not an easy feat to replicate...

Unlike traditional transportation service providers, ride-hailing companies built their business capabilities by adopting various leading edge technologies (AI, ML, DL, Augmented Reality, Mobile app, bigdata and IOT) for operational automation, service delivery, prediction and planning. User data is collected through mobile app and harnessed to innovate faster, improve services and maximise values to the whole business eco system. 

A well functioning alternative service to riders.....

The arrival of ride-hailing companies in Southeast Asia were welcomed, as for once passengers had a choice to abandon conventional transportation service providers, that mistreated clients for decades (all of which were regulated businesses). Since the arrival of ride-hailing companies, more passengers comfortably leave their vehicles at home and use the ride-hailing services. After all, passengers can easily book a ride via their mobile app and get served within 7 to 10 minutes, as opposed to the old call booking system where getting through is extremely difficult.

The solution to driver economics problem is dynamic in nature...

Question is, why would we need government intervention to solve a problem already resolved? Secondly, there are two methods to solve this driver economics issue - one by increasing passenger prices, the other is by streamlining the large number of drivers according to current demand. Both are dynamic elements and neither strategies can be executed by the government efficiently without realtime data, reliable predictive capabilities and the backing of a credible data science team.

Let's not get politics in the way of good business....

Finally, driver groups involved in protests may carry other hidden agendas (speculative but that's the popular trend) than just preserving their economic interests. Government intervention here might end up protecting business interest of politically linked individuals or groups that destroyed service quality, encouraged business monopoly without competition and frustrated consumers in the past.

Are Superapps Draining our Money Pots?

#LKminiblog - Are Superapps Draining our Money Pots?

Superapps and ecommerce startups snatched a big chunk of startup funding for Southeast Asia last year, especially those endorsed or lead by Softbank and BAT (Baidu, Ali, Tencent). 

Capturing the sizable Southeast Asian consumer market...

In 2017/2018, investors were particularly focused on startups with standardised platforms to engage with Southeast Asian consumers mainly via mobile devices. This trend is expected to continue this year, but with more coverage areas by ride hailing companies and new value added services including to businesses.

The 10 or so well backed Unicorns will continue to grow and prosper...

Plenty of capital reserve will enable companies like Grab, Go-Jek, Zalo, Bukalapak, Tokopedia, Lazada, Shoppee and others to continue improving their applications, interfaces, technology stack, talent pool and market reach. These startups can become a critical gateway to new eco systems in health, retail, and finance, not reachable by conventional businesses.

What about the B2B tech startups?

However, this strategic focus by major investors certainly affected tech startups on the B2B segment, especially those developing and innovating vertical solutions. Most investors, including regional financiers, simply followed the footsteps of larger investors with their bets in the past years. This drained the money pot and left thousands of B2B folks to battle it out for the leftovers.

Driving the change we want...!

Hopefully investor tone will change this year with promising startups emerging for applied AI and AR in various sectors, fintech that blends several techs, healthcare innovation and smart city solutions. 

As for regional and corporate MNC investors, the former will continue to invest opportunistically or for nationalistic reasons and the later will align investment to scale the size of the community on their platform. The rigour of activities here will depend heavily on economic growth and the entrepreneurial community.

In the end, the challenge for Southeast Asian B2B startups in 2019 remains the same as the previous years. Changing the perception of stakeholders and our entrepreneurial community to break the heuristics that we are incapable of creating scalable world class solutions.  Instead, we should be driving harder for excellence, up skilling, team building, task completion, coaching and envision business solutions fitting for global markets and scale. 

Are we up for it?

Wrong Data is still a better start than No Data

When the repertoire of enterprise data fails to generate any meaningful information to answer business questions, returning to the drawing board is the wise decision.

Observed carefully, more than often, the root cause points to methodology flaws rather than a direct breakdown in IT systems or integrations.

Problem is, we still treat data as departmental assets and take a territorial approach to fixing problems.   While this is necessary for quick turn around, a holistic approach needs to be in place for addressing data issues impacting across the organisation (involving both IT and business teams), or we’ll end up storing a huge amount of data unusable nor used by the workforce.

Some key areas that should be coehersed to ensure continuous value generation;

1. A focus group with representatives from every unit of the business.
2. Specifying organisational goals that are aligned with data capturing, collecting, analysis and representation accross the organisation (outlining the big picture for your data projects).
3. A common mechanism/process to capture data challenges faced by every unit.
4. A clearly outlined Data policy for internal users ( sharing, redundancies, security...etc)
5. Periodic measuring/ reporting of data assets and its returns ( e.g Data ROI, YoY return, ratio of active and unused data, amount of Intel lead to new growth engine, productivity increase and etc.)
6. Strategic mining of the unused or less used data for new insights on business questions
7. An active initiative to resolve the top three to five data issues affecting the business (e.g. Incomplete customer data, redundancy, bias data, sluggish data delivery to users and etc)
8. 
   

Achieving data utopia might still be a far fetched dream, especially when data is collected through almost every imaginable device/equipment possible. As such, there is no shame in starting with huge amount of wrong data rather than no data. Understand the big picture and take a phased approach, solve one issue at a time. Measure outcome as you move along and the value your data generates will only increase.

5 Levers to Optimise Learning

“Nothing is ever Achieved without Enthusiasm”, Emerson

Ever wondered how Uber, ANT Financial (Alipay), Xiaomi, DiDi Chuxing, or Airbnb turned into world's largest unicorns in 2017 (and yes, please note that 3 out of 5 are actually from China) ?

Perhaps it was the early market lead, a disruptive technology, platform inspired business model, successful fund raising rounds or simply favourable government policies. Each firm hacked growth based on different mix of factors but shared one similarity. Their leadership and workforce was able to keep pace with the supersonic growth and recalibrate repeatedly to the next future state.

Entrepreneurs whom are in constant pursuit of new knowledge and finds a thrill in the perils of solving difficult business problems are effective learners. They promote sharing of information, inferences and team collaboration for optimal execution of every business function. Making optimising learning capacity of individuals and teams in organisations an imperative measure in driving and sustaining growth. A metrics closely observed by leaderships and funding ventures alike.

Technology to Assist and Augment 

Businesses operate in an extremely fast environment today, where advancements in consumer gadgets and enterprise technologies have enabled us with massive computing power capable of deciphering quintillion bytes of data in nano seconds. Artificial intelligence and machine learning is further sophisticating automation of softwares, machines, neural networks, robots and humanoids.

Ignoring such developments and their benefits in assisting and augmenting work in sectors such as health, legal, high tech, retail and financial will only leave the business irrelevant to market over time. Instead every technology disruption provides a purposeful learning opportunity to move higher in the work chain that should be embraced.

Make Sense of Data

Similarly online business models, platforms and devices are flooding us with data and information. Researching a customer or partner, means pulling and collating information from various sources internal and external (e.g. within the enterprise walls, certified agencies and what is available publicly).

Using analytics to make sense of the different data sets and correlation to business helps to build better reasoning for business cases, speedily scratch the surface of critical operational issues, dive deeper into situations, or anticipate an upcoming threat (or avoid the ‘boiling frog’ phenomenon). It expands cumulative ability to uncover answers to inherent business questions and expose unchartered frontiers for seeking new understandings. This improve resources allocation and focus for all the right business activities in product innovation, sales, marketing and support.

Practice Problem Solving

Growing startups exposes entrepreneurs to various types of business constraints. Some problems are clearly defined with goals, while others are inhibited by vagueness, thrusting us into a panic zone. The iterative process of identifying, classifying, defining, diagnosing, understanding and breaking down the problem, results in expansive mental progress that improves strategies and methodologies in problem-solving over time.

However, exhausting teams with repetitive problems (which is a target for complete automation anyway) will only erode this cognitive exercise to an inertia. Instead refocus them to address complex challenges, where the process of active revealing and listening in search of a solution mechanism takes place. It is here, where many startups stumbles over a lead, growth engine, untapped market, or a golden opportunity to gauge market share from conventional players.  Riding back on the iceberg parable illustrated in the previous point, the deeper you dwell into business inhibitors, the more questions you will uncover. The journey to answer these questions will lead to breakthroughs.

Failures multiply Worth of Lessons

It's bizarre but success and failure lies in the same direction. Success is reiteration of adjustments made from failure to failure without ever loosing the excitement for the venture.

If Abraham Linchon would have shied away from numerous disappointments and feared the angst that may arise, it would have taken a lot longer to abolish slavery and build a modern America. If Nelson Mandela would have stopped fighting apartheid in South Africa at the thought of being imprisoned for life, South Africa will still be torn in civil wars and severe human rights crisis.

Failure teaches value of resilience, focus, reflection and to bounce back stronger each time a pursuit hits a dead end. Only by apprehending the lessons of defeat, one can gain clarity to amend path forward and avoid repeating mistakes. In fact, no one successful is ever reserved from having to confront calamities, criticism, and temporary standstills. After all, success is sweet when you can tell a story that can inspire others.

Performance Support Tools

Performance support tools, such as collaboration platforms, portals, advance analytics (including bigdata), case and content management solutions (e.g.  JIRA, G Suite, Slack, Asana, and other SMB SaaS Services) that are integrated across the various business functions in the organisation is a great way to distribute and update team members of newly available learning assets. In addition, the design and representation of these tools across functions can influence how quickly complications in process or product can be resolved.

The Act of Perfecting the Game

Using the levers mentioned above will speed learning pace and get us quickly to the deeper composite nature of any business riddle. This creates more room to effectively piece personal mastery with cumulative learning assets garnered from others in a collaborative manner. Pushing teams to increase adoption of core capabilities to understand complexities, prioritising what matters most and develop effective conversations to perfecting the game.

Practise does make us perfect (or at least better) but equally important is to break away from bad habits of not seeing the big picture quick enough, getting stuck in management myths, or living in a delusion that learning comes with experience (The Fifth Discipline, Peter Senge). As they say, you can’t gain without pain or by being oblivious.

Relationship Formula for Small Businesses

If you want to go Fast, Go Alone. If you want to go Far, Go with Others.

I must admit that while I was writing my last blog on social media advertising as a crucial customer touch point, my mind was already filled with hundreds of questions on the premise of how various dimension of business relationships impacts upward improvement in revenue, profitability, stock prices, intellectual properties, brand, product utilisation, partner networks, markets, productivity, customer satisfaction, employee satisfaction, reputation and many other outcomes too granular to be mentioned.

What a grave mistake it would be to simply engage into action, accompanied with just a ‘gut feel’, before analysing these relationships, its layers and tiers; correlatives; strength; values or risks to your business? Instead, should we be calling our actions and channeling our investments based on the conditions of key relationships to the business? How does one relationship affect the other? For example, negative energy accumulating in the workforce can certainly impact customers and partners which are critical to growth; ruthless investor activism that pushes leadership into buy back programs and dividends during sales slump to quickly raise return of stocks will not only result in exhaustion of enterprise coffers, but will contribute greatly to income inequality in the workforce and the society in general.

There are countless number of ways to bring structure and automation to track most of these relationships, but a bubble diagram is perhaps sufficient to initiate study and map m utual values, which paves the way to mark priorities according to business goals. In fact, any investment on marketing, advertising and automation should take into consideration of such priorities and value creation activities. Making this a critical exercise especially for small businesses in rapid growth mode with small caps and trust me that this blog will not lead you to a CRM dialogue of any ‘X’ factor as a necessary point of resolution but may influence such conversations in the future.

A Case to Reflect

Long standing enterprises that has been around for over hundreds of years such as Colgate-Palmolive ( or Colgate rather), Coca-Cola, Citi, IBM and GE were some examples of businesses which survived the test of time mainly due to their founding executives ability to visualise, create, manage and control internal and external business relationships to generate an overall positive vibe that fuelled growth.

Coca-Cola for instance, was once sold for 5 cents a glass and started business in the late 19th century with just total of 7 or 8 serving a day through soda fountains. Today, this business has grown close to 1.9 billion serving per day and I need not explain the prices nor its brand prowess. The creator of this drink John Pemberton, a pharmacist and a war veteran was hoping to find an alternative or cure for morphine addiction, as many people suffered such an addiction back then due to the war, just like Pemberton. In fact, the first version of this drink was a coca-wine (alcohol and cocaine infused drink) like many other carbonated fountain drinks of the time (e.g from Spain and France). The non-alcoholic version was created only after the banning of such ingredients in fountain drinks (even though I strongly believe that the coca leaves are still a key ingredient ). At the time, Mr Pemberton also claimed that Coca-Cola cured many diseases, including morphine addiction, indigestion, nerve disorders, and headaches, though these aren't the reasons why we drink coca-cola today. How this business grew to what it is today? I would think finding the the secret recipe to an elixir that appealed to a global taste bud was the easiest part.

Pemberton, sold his business, prior to his death, to several businessmen including a young druggist, Asa Candler. Pemberton, also brought his sons to hold different property rights of the business. Candler, saw the potential of the drink and started building his downstream relationships following the soda fountain trails of restaurants, bars and other recreational outlets despite infighting among  the different stakeholders including a thorny relationship with one of Pemberton’s son. Candler also observed the increasing demand as a further opportunity to bring Coca-Cola directly to customers by bottling it with partners. Candler watched competition and imposters closely in order to understand how they try to clutter the market and introduced the unique design of the the bottle that is still in use today, to help customers choose the original product. Candler’s focus on relationships that created an advantage to his business soon overwhelmed and helped to severe toxicity from the equation and made more room for expansion.

A ‘Scribble’ is as good a Start as a ‘Doodle’

Sometimes finding the starting point is the hardest – and this is when a general mind map of all relationships that affect your business can come in handy. Scribble it or take it a notch further, just for the fun of it and doodle it (no one said business have to be boring or characterless).

Startups might find this exercise pretty straight forward if you are dealing with a single or range of interrelated products aimed at the same market category. A simple bubble diagram, indicating upstream, lateral and downstream relationships (see illustration 1 – Sample Key Relationship Analyses), along with markers to identify layers of connections is useful visual analyses which helps to get detail idea of your current relationship trends, returns, advantages, risks, value creation and investment activities. In small companies, this can be a revealing exercise that points you where much of your resources is being consumed and if the returns are worthwhile. Save the diagram, and you will see that the story it tells, will change as you revisit them every quarter. In fact, you may also observe changes in business relationships for the better or worst depending on incidents and actions that your business may have undertaken. This will also help the business from refraining or changing tactics where relationships are cold and value creation there would just be like running ghost trains.

Prioritise Relationships - Finding the Perfect Balance

But the idea to get into this exercise is not just to identify types of relationships and where bulk of your investments are being absorbed or even what improvement is being attempted in the past. It is, sort of a barometer to the validity of your current business models and identify which relationships deserves your utmost attention at this moment in line with growth agendas (which changes from time to time). Evidently, there are hundreds of relationship commitment hypothesis, studies and best practises contributed by researchers on how to form long standing buyer-seller relationship stratagems in a variety of businesses and non profit backdrops (e.g. Equality, Trust, Openness, Rationalism - Smith 1998; Overall Satisfaction - Garbarino & Johnson 1999; Cost of Relationship Discontinuation – Morgan and Hunt 1994; Flexibility - TA Scandura & MJ Lankau 1997), and much of these practices have been assimilated according to industry settings into best breed of technology solutions for easier and faster consumption by businesses. However, what’s missing though is the simplification process of these various commitment variables which is equally critical in growing startups and small businesses.

E.g An IT service provider whom developed a tool that could help enterprise customers migrate swiftly from one cloud to another can choose to sell the tool and relevant services directly to customers facing such business pain. But upon proving the successful adoption of the tool, they may also see the potential to sell through  partners and vendor marketplaces who may be in an ideal position to offer clients a transaction economics that the tool creator themselves are not able to deliver due to specialisation or other resource constriction. Hence, this becomes a question and point of decision between growing the partner network and the direct sales, or both. If so, what would be the ratio to be applied for ideal outcome.

Arms Length Transactions are not necessarily Evil

Many small businesses today are present online with complete automated systems. Clear content on products and terms of sale are self explanatory with online support for customers who may have additional requirements or questions. Apparel, groceries, movie tickets and other cyclical or standardised products (e.g cloud services) get sold without the need for a sales person to interact with each and every one of the customers. These customers don’t expect personal treatment, but just personalised services and products up to their expected quality of standards along with data protection and security assurance. Here, there is a necessity to capture and analyse customer data, purchase history and trends to make appropriate future updates or offers to woo the customers to return and form loyalty to the brand. Often the customers who fall in this category has a tendency to switch from one shop to another easily and as such understanding what is transaction economics to them is critical in deciding and architecting the right loyalty program, advertising media or partners to worky with. E.g. a wine shopper might also appreciate relevant wine accessories, gifting services or the right cold cuts and cheese to go with the wine, all in one place instead of having to visit 3 or 4 shops.

In other words, unlike popular believe, businesses can establish loyalty with arms length relationship by anchoring on the right value creation activities and fulfilling expectation voids left by competitors. As such, instead of running intensive loyalty campaigns for adhoc transactional clients, you might be better off, turning to methods of acquiring and maintaining them cost effectively apart from making continuous differentiation in your offerings. In addition, the freed sales resources can now be repositioned to work on other market segments that needs personal and advisory services to grow(e.g. B2B solutions).

Expect Toxicity and Difficult Relationships

In my years of observations (and personal experience), I am yet to find an enterprise or startup that is not faced with difficult or toxic relationships (futile search, but why not?). Mark Zukerberg and Eduardo Saverin of Facebook, the Ambani brothers of Reliance India, and the fallout between media mogul, Rupert Murdoch and Richard Li of the HK Satellite TV,  are just some high profile known examples to back this.

Partner disputes over shares, intellectual properties and other business rights; a toxic employee or manager de-energising your workforce; a channel partner or sales person taking your business hostage by claiming exclusivity to relationships; technical staff making unreasonable demands in return for critical business assets; or clients threatening to switch provider if you don't lower prices or insist to work with only certain individuals; fraud; and other acts of sabotage are all common difficult relationship situations that can sow dissension and clutter in the business. The absence of or loose governing policies, contracting, legal and other enterprise services in startups and small businesses tends to make this worst. Some organisations become critically effete and drained due to the distractions. Others, take a stance to stay on course with goals and thrive in success by addressing conflicts firmly while putting in place mechanisms to protect critical intellectual, material and relationship assets.

Rewarding Relationships are Manufactured by Minds

“Hear much, leave all that is doubtful alone, speak warily of everything else, and few will be offended. See much, leave all that is dangerous alone, deal warily with everything else, and thou wilt have little to rue. If thy words seldom give offence, and thy deeds leave little to rue, pay will follow.”, Confucius.

Point is, you could be in possession of a ground breaking idea, a disruptive technology patent, abundant materials, and a ready market to adopt your offerings. Though, without identifying and cultivating the right relationships, mutual interests, trust, needs and co-dependence, mobilising these resources could end in long time to return or worst, fails to return.

The effects of advances in multiple enterprise technology disciplines that is transpiring through cloud services for superior infrastructure and applications services; advance analytics; automation of business processes; along with the rise of mobile and sensor based devices today, are driving digital transformations of every imaginable industry. Enabling us with valuable data, that can complement the humble bubble diagram that we talked about earlier in the blog.  This present us opportunity to compare real figures of performance against relationships; recognise what we do and don’t know; where advantages exist; and where an upper hand’s support is critical to succeed.  This is where we bring our multi-dimensional intelligence into work – the best of social, emotional, intellectual, qualitative, empiric and quantitative capabilities to unearth best call of strategies and actions to hold strong the founding virtues of the business.

Small Businesses and Social Media Advertising - Is It Worth Your Attention ?

“The Intelligence of the Universe is Social”, Markus Aurelius

The word 'social' can simply be put as friendly gathering of individuals or groups to share and expand interest or values. It describes the intrinsic human nature to bond and operate together in a communal manner for recreative, religious, cultural, economic or political reasons. In fact, researchers such as Bruce T. Lahn and colleagues of Howard Hughes Medical Institution, believe that the evolution of  large brain in the human species occurred in two stages, mainly to facilitate the complex process of mastering human relations and languages to interact, communicate and collaborate effectively with large groups of people. Perhaps this explains as to why we continuously improve innovation in telephone, telegram, postal service, mobile, Internet and other advanced technologies than constantly bridges communication with one another. 

The word 'social media' was only officially entered as an adjective in etymology of words since 2008, unlike its other proxy terms 'social networking' (1984) and 'social network' (1971). Social media refers to online platforms that provides an avenue for users to create profiles of themselves, connect, interact and collaborate  with their social contacts via text, audio, video, live streaming and messaging exchange. Some of these platforms such as Facebook, Twitter, LinkedIn and Instagram are seamlessly integrated in our daily lives. 

“Coffee and a Smartphone for Breakfast”

Checking your Facebook status, along with other news feeds on smartphone every morning is a familiar image in most households today. All notification and messages are readily waiting for further perusal, as we click on the neatly arranged icons of various social media applications that we use to connect with others on our mobile devices. Our social, personal, professional and communal lives are increasingly integrated in our smartphones, benevolently unifying various social tasks to the best use of our time and resources. The non-believers who complain about the commotions that social media causes in their lives are really small in percentage, and perhaps suffer from a lower social intelligence required to manage the labyrinthine of the increasingly connected world. Though, this group is expected to follow the adoption of social media eventually. After all, you are in full control to manage your social media uses in similar ways that you manage interactions with other communication tools prior to the Internet.

“Understanding Social Media  – I Just Tweeted that I blogged”

As users use these platform at no cost, platform owners on the contrary generate income by serving ads to their users. The accuracy and relevance of these ads are improved tremendously by tapping on the insights extrapolated from the content generated and shared voluntarily by its users. For example, if a user posted about a book they read recently or liked a certain reading group, an advertisement of an upcoming book of similar category might be served to that particular user based on patterns isolated from correlation of data amassed from their profile, likes and posts. 

This targeted advertising approach practised by social media platforms, presented unprecedented growth opportunities for millions of small and medium sized businesses with a lot less capital. The promise of greater reach, relevant audience, expansion, lower operational overhead, hassle free promotion and quicker results, are just to good to be passed up. Plus advertising in other traditional mediums such TV and print is still formidable in cost for smaller ventures.

“User Generated Content Hardly Escapes Humanity“

However, if this approach is based on the insights pulled from user generated content as stated above, should we be concerned with the quality, validity, appropriateness, authenticity and legitimacy of the data that social media and thousands of digital marketing companies, tout to us, as the utopia of marketing? After all, we are products of social occurrences with the capability to be machiavellian in varying degrees to achieve a desired state. Should we question the reliability of data in social media? How often do social media users post misleadingly or include fake information? Evolutionists and social researchers (see Brynes and Whittens, 1998), state that we may be exposed to a high degree of noise data meant to mislead, emphasise one’s image or simply obtain something cunningly, although this human trait is ubiquitous and is not limited to social media. Does this mean we abandon promoting our business on social media altogether? 

“The Opportunity of Securing More Mileage for Less Bucks”

Illustration 1 - Sample Projection of Social Media Advertising 

Truth is, while we should continuously question social media platforms on the quality and authenticity of their data (the more frequently you question,  the higher the chances that they will improve it), data discrepancy does not affect your business, just the effectiveness of your marketing dollar. The 1.5% estimated average click through rate for some social media platforms such as Facebook or Twitter  is still a good start (see illustration 1), as long as business owners are aware of the data issues that their direct and indirect social media advertising providers are exposed too. Understanding the risk and limitations of these platforms can aid in better understanding of online advertising; fruitful conversations with service providers; suitable strategies and smarter investment of your chump change, resulting in real growth and higher sales conversion rates.   

“Visual Content is more than 40 times more likely to get Shared on Social Media than other types of Content. (Source: HubSpot)”

Inevitably, advertising on social media will only work if you have an adequate online presence. A website, online store, blog site, social media pages (e.g  Facebook, Instagram, Twitter, Pinterest), or just a combination of few at the least. Good news evidently is that, this isn’t an expensive affair and there are free tools to get started and sample some online strategies without much technical knowledge.  Content and a sharp messaging is nothing new nor exclusive to the online world. Good content drove businesses even before the invention of Internet and social media. Even though highly visual content with messaging that arouses the senses and engages the audiences, creates the best momentum.

“Many a Thing Small Has made Large by the Right Kind of Advertising”, Mark Twain

Success though, is not imminent as some eager ignorant marketeer would suggest. Most shops will have to plan and execute campaigns in phases to identify and learn what works best for their businesses with a clear idea of what call to action needs to be driven in each campaign stage e.g. like page, buy a product, submit email address, download app and etc.  As such receiving lukewarm response or non-response from the audience in the first few runs should be considered normal and should be budgeted as an opportunity cost to successfully reap growth benefits of social media advertising. What is important though is to constantly assess and improve your strategies in addressing shortcomings e.g conversion hindered by sluggish system despite high traffic to site; refinement of key words; unclear call to action upon click through and etc.The essence of advertising has always been the same. The narration and telling of a timeless, magnanimous story and how your product supposedly will affect your customers in the most intimate way possible. This story telling opportunity however was for a long time limited to businesses with funds to burn. Social media platforms have flipped this playing field by allowing just any business to participate with the lowest entry cost ever imagined. One thing is definite. Not taking this chance will surely be a mistake. 

The Fuss about Ad Block - or is it just the Ad Industry Panicking !

It's been quite a while since I wrote anything here and hope to put something every week from now on. So kindly bear with me.

This week, I am all over the online ad business and how the industry is reacting to ad blocking. I am disgusted about this whole ad block business where some sites are starting to charge the users a fee for content or block access to their sites altogether if you used ad blocker app to filter out adds. Currently ad block app usage is catching on but perhaps only 25% of Internet users are probably using it. However, that is enough to hurt the online advertising industry to make such big fuss even though big names such as Google, Twitter and etc are denying any Impact to their business. Well, why not, just have to shuff it down somebody's throat, and money is made. After all the real loosers are the advertisers and the Internet users.

Is the online advertising really helping to grow businesses ?
For years, online media have been painting the story that they are actually helping brands and advertisers to grow the sales of their products or services. That's what I thought too initially; but truth is that all the clicks and views generated very little interest in what was being advertised or presented to the viewers. Worst they annoyed viewers. This is probably why the ad serving market was not keen to charge its customer based on real leads or business opportunities. Instead, this market and its players continuously eluded advertisers to pay based on views or clicks; while viewers are served forcefully with ads they don't actually care for. Sometimes the ads are just delivered randomly that there is no real relevance to the viewer in fact.

Clearly these medias don't seem to have the intelligence to deliver ads to the right audience in many cases, as per their claims or even isolate questionable advitisers from the dark net. E.g. From astrology scam; dating scam; work from home scam to escort services is freely contributing to this industry which supposedly a reputable network of business/es based on advance, secure and sophisticated technology foundation. Again, they are eluding us here.

Are the ads delivered to its rightful audience and are they effective instrument for growing your business?
Anyway, let's keep our focus on the ad blocks. Would online media start to punish its patrons if the ad block apps does not hurt their business? Is it also not a sign that all these while their viewers had no interest what so ever in the ads that was being served to them?  If tables were to turn and advertisers demand that publishers only charge for real leads and not ads served, there is a chance many of the publishers will close shop. Because they really never did help their customers to grow the business in anyway. Publishers took the short cut by just providing access to their audience or patrons but not really sell anything for their customers.

If I am media, I will start to take a step back to fix things and that is to ensure value delivery to both the advertiser and patrons alike. The advertiser is looking to grow their business and the patrons are looking for a smooth experience with your site. If match can be made to the needs of both, you are a winner.

Don't get eaten by the new fish in your own pond....
Perhaps there is also a key lesson to be learned here from the offline media. The online media have to learn to present ads that are not intrusive plus catchy enough to intrigue anyone's attention. The advise to big players, don't underestimate your patrons vulnerability - they might be willing to stay away from your sites rather than being bombarded with annoying ads. Every situation or challenge opens up opportunity to a new player that someone might figure out a new business model that keeps both advertisers and viewers happy while that can mean disaster for the current players who will have to then play catch up. On the other hand, advertisers should create ads that speaks to their audience so that it's not turned away. In brief, this industry has to start fixing the holes; embrace ad blocking where neccesary and stop force serving ads that annoys people. Because sooner or later someone is going to differentiate themselves in this pond by just doing the above mentioned to deliver the right values. So buck up or be prepared to leave the pond.


NOTES

* PageFair and Adobe cites that , there are now 198 million ad blocker users, up from 21 million in 2010.

* AdBlock Plus claims to average 2.3 million downloads a week and  the estimates are that that ad blocking would have  cost publishers USD 22 billion in revenue in 2015.

Happy weekend  everyone!