Sales f Reuben Yonatan h

AI in Your Sales Operations: Do You Really Need It?

Artificial intelligence, or machine learning, has been the buzz of every digital product from commercial tech to business productivity apps. Artificial intelligence has entered our everyday lives and its potential seems to improve by the day.

It’s hard separating hype from reality. On the one hand, the prospect is undeniably enticing: intelligent algorithms, learning from results, calculating opportunities, price points, and personalization strategies consistently without rest. Yet, for the non-tech savvy, most of our information comes from product developers’ marketing copy -- hardly an unbiased source. What’s to be believed?

It’s appealing when products you already use roll out AI features on their own; you can dip your toe in familiar water. The next best alternative is when third party AI products integrate with your solution; you have a clear demarcation to evaluate the AI’s performance before and after deployment.

But the question of whether you actually need AI in your sales operation remains unanswered. It sounds promising, but AI in sales automation is heavily hyped, largely untested, requires investment, and entails disruption.

Perhaps the question, "Do I need AI in my sales operation?" is replaceable with, "Can I achieve measurable improvements without investing in new technology?"

The answer to the latter question, in most cases, is yes.

Tighten Up Your Sales Process

We recently wrote an article analyzing the sales process as a means to improve sales effectiveness. It was based on CSO Insights’ 2016 Sales Optimization Study, which surveyed upwards of a thousand companies of varying size and industry for insights on their sales objectives, challenges, and methodologies.

Most surveyed companies had a significant percentage of salespeople (ranging from 25% to 75%) who'd failed to meet quota. Over 68% of companies failed to attain their revenue target. The study concluded that most surveyed companies’ major objectives and challenges could be met or resolved by improving sales effectiveness.

Tightening up the sales process results in a more trackable, predictable, and governable sales operation than one in which salespeople chart their own course. There are a few characteristics of a well-functioning sales process that enable consistent, adaptable performance.

1. The sales process is defined, documented, and reinforced. Salespeople are introduced to the process from training day, and it’s the basis for their daily workflow. There is a distinct sales process for each sales channel. Sales pipeline stages reflect the sales process and are defined by entrance and exit criteria. As the sales process accurately reflects the buyer’s journey, it can assess general confidence rates per pipeline stage and indicate progress toward sales goals through KPIs.

2. The sales process is dynamic. The sales process is periodically reviewed so that it is continuously reflective of the buyer’s journey, changing company conditions, or updated sales goals. Managers track key metrics through dashboards and reports; they proactively update salespeople on changing metrics and receive performance alerts for KPIs. Salespeople receive regular feedback on their use of the process.

3. Salespeople operate with common resources. There is an accessible document library of proposal templates, contracts, and training materials. Templates are based on sales playbook guidelines with clear objectives, measurement tools, and an approach for closing deals. Proposals are tracked throughout the sales process with win/loss statistics collected and are periodically updated.

4. Salespeople collaborate to further deals down the pipeline. Salespeople are in touch with collaboration tools -- communication apps, a shared inbox, group messaging in the CRM -- and best practices are routinely identified and shared across the team. Feedback is offered constructively, advice is volunteered with subject matter expertise, and team members feel comfortable with ownership of opportunities.

If your sales process doesn’t sound like this, you’d find benefit without additional technological investment adapting your sales process to meet these characteristics. This becomes a matter of leveraging existing technology and creating a collaborative atmosphere among your team, rather than searching for an external solution to patch over systemic shortcomings in the sales process.

Implement Data Management Protocols

Your salespeople are interacting with prospects, leads, and customers using data in the CRM. The CRM is central to an organized, collaborative sales process. If your salespeople are turning away from the CRM, there are ways to encourage user adoption.

But encouraging adoption is only half the battle; the other half is ensuring that data inputs are consistent, standardized, and clean. The expression “garbage in, garbage out” applies. Even with complete user adoption, inconsistent data will undermine your analytics, your forecasting, and future opportunities that benefit from a 360-degree customer view. Therefore, it’s essential to train employees, audit data regularly, and create a data management plan with data entry rules.

• Establish naming conventions, data entry rules, and deduplication rules. You’ll need to create standards and stick to them regarding punctuation (NY vs. N.Y.), definite/indefinite articles (the, an, a), writing vs. spelling out numbers (3 or three), and the use of abbreviations. Also establish best practices for checking the database before new records are created; redundancies will throw off reports and confuse other team members. Create a simple protocol for verifying data accuracy, merging records, or -- as a last resort -- deleting records.

Train your team with documentation, onboarding, and individualized assistance. Your naming conventions, data entry rules, and data deduplication rules should be made available in any format to anyone entering or auditing data. New employees and existing team members can be onboarded to the new protocols through orientation meetings and specialized training. Power users can be deputized to assist team members who struggle to adapt. Remember, salespeople were hired to sell, not conform to complex data entry rules, so be patient.

Audit your CRM data to account for redundancies, irregularities, or missing data. You’ll need to account for your data assets in older systems, email accounts, and Excel spreadsheets. Interview data handlers to determine the location of data assets and how they’re being used. Prioritize data by commercial value and consolidate it in a format to be uploaded to the CRM. Use filtered searches to find redundant records and merge accordingly. Make bulk edits where necessary, and fine-tune data entry parameters so fewer mistakes are possible (e.g. drop down menus versus free text fields).

When your data is dependable, you’ll be able to adapt your sales process and training methods and trust the metrics used to measure the outcome. While good data going in won’t guarantee good results, garbage data going in will preclude them entirely.

What is the Role of AI in a Sales Operation?

The premise here isn’t that artificial intelligence in a sales operation is unnecessary, but that there’s plenty to be gained simply by refining existing systems without an additional investment. As mentioned earlier, artificial intelligence promises exceptional business opportunity in the future -- whether that matters to you now turns largely on whether your house is in order.

In fact, there are several promising artificial intelligence products that integrate with CRMs. They offer benefits for productivity, efficiency, and forecasting (if the marketing copy is to be believed). However, many of these companies have raised multiple rounds of funding from investors, so evidently many parties are committed to their success.

1. -- Call analytics software Chorus records meetings and calls, creates searchable transcripts, captures time-stamped notes, and highlights references to topics of specific interest or BANT criteria. Users can add trackers to return to specific moments in recorded calls. Sales departments can identify trends, build training programs for new hires, and ensure customer details don’t get lost in the ether.

2. Cogito -- Cogito performs in-call voice analysis to determine the emotional state of the other caller. It analyzes speaking patterns, word choice, and other conversational dynamics to offer real-time guidance to sales reps. This includes telling reps to speak more slowly, empathize more, or give the other caller room to talk. The recommendations intend to build better rapport and improve positive outcomes. Cogito also monitors conversations and gives managers an opportunity to observe and step in if needed.

3. Conversica -- Conversica builds a virtual assistant that can engage and nurture leads, follow-up, and cross-sell in natural two-way email conversation. Conversica has shown high success rates at obtaining valuable data points such as the best phone number, time to call, purchase criteria, and feedback. The virtual assistant operates in the background around the clock, and can be tailored for a number of roles in a variety of industries. Tracking and metrics are available in real-time dashboards to review the AI’s performance.

4. TopOPPS -- TopOPPS provides virtual sales rep assistance through automation built into email, calendar, and mobile apps to improve communication, keep the pipeline updated, and make informed decisions. The company also produces a product aimed at improving sales process execution based on proven strategies while onboarding new hires faster. Both solutions offer prescriptive insights in the form of AI-based forecasting, data-driven coaching and plan performance strategies.

There will be a growing role for artificial intelligence in sales operations in years to come. But of course, businesses found ways to make sales before AI and they’ll continue making sales without it. Artificial intelligence offers a lot of promise applying data-driven insights and automation to the sales process -- but without organization, data management and training, AI will simply be a band-aid for an incoherent strategy.

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